An Event-driven Psychophysiological Assessment for

Health Care

Silvia Serino

1,2,*

, Pietro Cipresso

, Gennaro Tartarisco

, Giovanni Baldus

Daniele Corda

, Giovanni Pioggia

, Andrea Gaggioli

1,2

and Giuseppe Riva

1,2

Applied Technology for Neuro-Psychology Lab, Istituto Auxologico Italiano, Milan, Italy

Department of Psychology, Catholic University of Milan, Milan, Italy

National Research Council (CNR), Institute of Clinical Physiology (IFC), Pisa, Italy

Abstract. Computerized experience-sampling method comprising a mobile-

based system that collects psychophysiological data appears to be a very

promising assessment approach to investigate the real-time fluctuation of

experience in daily life in order to detect stressful events. At this purpose, we

developed PsychLog (http://sourceforge.net/projects/psychlog/) a free open-

source mobile experience sampling platform that allows psychophysiological

data to be collected, aggregated, visualized and collated into reports. Results

showed a good classification of relaxing and stressful events, defining the two

groups with psychological analysis and verifying the discrimination with

physiological measures. Our innovative approach offers to researchers and

clinicians new effective opportunities to assess and treat psychological stress in

daily-life environments.

1 Introduction

Assessing and monitoring emotional, cognitive and behavioral dimensions of human

experience, both in laboratory and in natural setting, has a crucial role in research and

treatment of psychological stress. According to Cohen and Colleagues [1]

“Psychological Stress” occurs when an individual perceives that environmental

demands tax his/her adaptive capacity. In this perspective, stressful daily experiences

could be conceptualized as a continuous person-environment transaction [2]; [3].

Every day, in fact, individuals are continually invited to deal with several situations or

circumstances (for example, being fired from work or having trouble with parents or

partner) that provoke anxiety and psychological discomfort. In this perspective [1-3],

a stressful event [4]; [5] occurs when a person isn’t able to effectively cope with a

challenge that is perceived to exceed his/her skills. Physiological measures can also

give further information to a psychological definition of stress, but there are still few

studies, above all in everyday situations, considering the relation between these two

dimensions. To accurately analyze real-time interaction between environmental

demands and individual adaptive capacity and to precisely detect stressful events

during the daily life situations, it is fundamental to use a real-time multimodal

assessment. As underlined by Ebner-Priemer and Trull [6], different terms have been

used to refers to real-time assessment of psychophysiological data: Ambulatory

Serino S., Cipresso P., Tartarisco G., Baldus G., Corda D., Pioggia G., Gaggioli A. and Riva G..

An Event-driven Psychophysiological Assessment for Health Care.

DOI: 10.5220/0003884200250034

In Proceedings of the 2nd International Workshop on Computing Paradigms for Mental Health (MindCare-2012), pages 25-34

ISBN: 978-989-8425-92-8

 2012 SCITEPRESS (Science and Technology Publications, Lda.)

Assessment [7-9], Ecological Momentary Assessment [10], Experience Sampling

Method [11], Real-Time Data Capture [12], and Day Reconstruction Method [13].

These assessment methodologies, although arose from different research paradigms,

have in common the continuous recording of psychological and physiological data or

indices of behavior, cognition or emotions in the daily life of subjects. Barrett and

Barrett [14] effectively defined real-time assessment procedure as a "window into a

daily life" since participants provide self-reports of their momentary thoughts,

feelings and behavior across a wide range of daily situations in ecological contexts.

Recent progress in biosensor technology and, on the other hand, the incredible

diffusion of mobile electronic devices have lead to ubiquitous and unobtrusive

recorder systems that allow naturalistic and multimodal assessment [14-18].

Computerized experience sampling method comprising a mobile-based system

that collects psychophysiological data appears to be a very promising assessment

approach to investigate the real-time fluctuation of experience in everyday life in

order to detect stressful events. At this purpose, we developed PsychLog

(http://sourceforge.net/projects/psychlog/) a free open-source mobile experience

sampling platform that allows psychophysiological data to be collected, aggregated,

visualized and collated into reports [19]. Our smartphone-based system collects

physiological data from a wireless wearable electrocardiogram equipped with a three-

axial accelerometer. Moreover, the application allows administering self-report

questionnaires [20] to collect and investigate participants’ feedback on their daily life

experience in its various cognitive, affective and motivational dimensions. In

particular, in this study we proposed and tested the use of PsychLog [19]: (1) to

investigate the fluctuation of experience during a week of observation; (2) to detect,

on the basis of psychophysiological real-time assessment, stressful events that

normally occur during daily activities and situations; (3) to compare the

psychophysiological data between stressful events and relaxing events occurring in

everyday contexts.

2 Materials and Methods

2.1 Tools

In our study we used PsychLog (http://sourceforge.net/projects/psychlog/), a mobile

experience sampling platform that allows the collection of psychological,

physiological and activity information in naturalistic settings [19]. The system

consists of three main modules. The survey manager module allows configuring,

managing and administering self-report questionnaires. The sensing/computing

module allows continuously monitoring heart rate and activity data acquired from a

wireless electrocardiogram (ECG) equipped with a three-axis accelerometer. The

wearable sensor platform (Shimmer Research™) includes a board that allows the

transduction, the amplification and the pre-processing of raw sensor signals, and a

Bluetooth transmitter to wirelessly send the processed data. Sensed data are

transmitted to the mobile phone Bluetooth receiver and gathered by the PsychLog

computing module, which stores and process the signals for the extraction of relevant

features. ECG and accelerometer sampling intervals (epochs) can be fully tailored to

the study’s design. During each epoch, signals are sampled at 250 Hz and filtered to

eliminate common noise sources using Notch filter at 0 Hz and low pass at 35 Hz and

analogue-to-digital converted with 12-bit accuracy in the ±3 V range. The application

extracts QRS peaks through a dedicated algorithm and R-R intervals [21]; [22]. The

visualization module allows plotting in real time ECG and acceleration graphs on the

mobile phone’s screen. Psychological and physiological data are stored on the mobile

phone’s internal memory, in separate files, for off-line analysis. Data are stored as .dat

(supported by most data analysis programs), .txt and .csv format.

In this study, we used standard smartphone (Samsung Omnia II i8000) equipped

with 32 bit CPU, ARM 11 RISC processor (cache 16KB) 667 MHz, RAM 256 MB,

1500 mAh Lithium ion battery, running the operative system Windows mobile 6.5

2.2 Experimental Design

Participants were six healthy subjects (2 males and 4 females, mean age 23) recruited

through opportunistic sampling. Participants filled a questionnaire assessing factors

that might interfere with the psychophysiological measures being assessed (i.e.

caffeine consumption, smoking, alcohol consumption, exercise, hours of sleep,

disease states, medications). Written informed consent was obtained from all subjects

matching inclusion criteria (age between 18-65 years, generally healthy, absence of

major medical conditions, completion of informed consent).

Participants were provided with a short briefing about the goal of the experiment

and filled the informed consent. Then, they were provided with the mobile phone with

pre-installed PsychLog application, the wearable ECG and accelerometer sensor and a

user manual including experimental instructions. Subjects were asked to wear

biosensor for one week of observation. PsychLog was pre-programmed to beep

randomly 5 times a day each day (between 10 AM and 10 PM) to elicit at least 35

experience samples over the 7-days assessment period. At the end of the experiment,

participants returned both the phone and the sensors to the laboratory staff. After

filling a short usability questionnaire, participants were debriefed and thanked for

their participation.

2.3 Psychological Assessment

Psychological stress was measured by using a digitalized version of an ESM survey

adapted from that used by Jacobs and Colleagues [19]; [20] for studying the

immediate effects of stressors on mood. The self-assessment questionnaire included

open-ended and closed-ended questions investigating thoughts, current context

(activity, people, location, etc.), appraisals of the ongoing situation, and mood. All

self-assessments were rated on 7-point Likert scales. Following the procedure

suggested by Jacobs and Colleagues [20], three different scales were computed in

order to identify the stressful qualities of daily life experiences. Ongoing Activity-

Related Stress (ARS) was defined as the mean score of the two items ‘‘I would rather

be doing something else’’ and ‘‘This activity requires effort’’ (Cronbach’s alpha =

0.699). To evaluate social stress, participants rated the social context on two 7-point

Likert scales ‘‘I don’t like the present company’’ and ‘‘I would rather be alone’’; the

Social Stress scale (SS) resulted from the mean of these ratings (Cronbach’s alpha =

0.497). For Event-Related Stress (ERS), subjects reported the most important event

that had happened since the previous beep. Subjects then rated this event on a 7-point

scale (from 3 very unpleasant to 3 very pleasant, with 0 indicating a neutral event).

All positive responses were recoded as 0, and the negative responses were recoded so

that higher scores were associated with more unpleasant and potentially stressful

events (0 neutral, 3 very unpleasant). In addition to those scales (not included in the

original survey), we introduced an item asked participant to rate the perceived level of

stress (STRESS) on a 10-point Likert scale. In particular, to rate the gap between

challenge and skills, we introduced two specific items: (1) an item assessing the

perceived level of ongoing challenge (CHALLENGE) on 7-point Likert); (2) an item

evaluating the perceived level of skills (SKILLS) on 7-point Likert.

2.4 Cardiovascular and Activity Indexes

Cardiovascular activity is monitored to evaluate both voluntary and autonomic effect

of respiration on heart rate, analyzing R-R interval from electrocardiogram.

Furthermore standard HRV spectral methods indexes and similar have been used to

evaluate the autonomic nervous system response [23].

From ECG each QRS complex is detected, and the normal-to-normal (NN)

intervals (all intervals between adjacent QRS complexes resulting from sinus node

depolarizations) are determined to derive the most common temporal measures,

including RMSSD, the square root of the mean squared differences of successive NN

intervals, and NN50, the number of interval differences of successive NN intervals

greater than 50 ms [23]. In general, RMSSD are estimate of short-term components of

heart rate variability. This experiment aimed at testing the feasibility of monitoring

concurrent stress and physiological arousal within subjects’ typical daily

environments and activities. Previous works have shown that psychological stress is

associated with an increase in sympathetic cardiac control, a decrease in

parasympathetic control, or both [21]; [22]. Associated with these reactions is a

frequently reported increase in low frequency (LF, range between 0.04-0.15 Hz) or

very low frequency (VLF, < 0.04 Hz) HRV, and decrease in high frequency (HF,

0.15–0.50 Hz) power. HF power is reported to reflect parasympathetic modulation of

RR intervals related to respiration, whereas the LF component is an index of

modulation of RR intervals by sympathetic and parasympathetic activity (in particular

baroreflex activity) [21-23]. Furthemore, stressors are often accompanied by an

increase in the LF/HF ratio (a measure used to estimate sympathovagal balance,

which is the autonomic state resulting from the sympathetic and parasympathetic

influences) [23]. Although the time domain methods, especially RMSSD method, can

be used to investigate recordings of short durations, the frequency methods are

usually able to provide results that are more easily interpretable in terms of

physiological regulations [23].

Spectral analysis has been be performed by means of autoregressive (AR) spectral

methods with custom software. The AR spectral decomposition procedure has been

applied to calculate the power of the oscillations embedded in the series. The rhythms

have been classified as very low frequency (VLF, <0.04 Hz), low-frequency (LF,

from 0.04 to 0.15 Hz) and high frequency (HF, from 0.15 to 0.5 Hz) oscillations. The

power has been expressed in absolute (LF

RR,

and HF

) and in normalized units. For

example RR series: LF

and HF

as 100 * LF

/ (σ

- VLF

) and 100 * HF

/ (σ

- VLF

), where σ

represents the RR variance and VLF

represents the VLF

power expressed in absolute units [21-23]. ECG biosensors used by PsychLog

application have also an integrated three-axial accelerometer. SMA index [24]; [25]

has been calculated in order to establish when subject was not in movement. In this

way we calculated ECG indexes, avoiding the periods in which the subject was

running, walking, or also moving too much. Signal-magnitude area (SMA): It is

calculated according to

 = (|()|) + (|()|) + (|()|)





(1)

where x(i), y(i), and z(i) indicate the acceleration signal along the x-axis, y-axis, and z-

axis, respectively.

Fig. 1. Mean and variance values of SMA index related to the previous five minutes of activity.

2.5 Data Analysis

In order to detect both stressful and relaxing events, Activity-Related Stress Scale

(ARS), Social Stress Scale (SS), Perceived Stress Scale (STRESS), Challenge Scale

(CHALLENGE) and Skill Scale (SKILLS) were within-subjects standardized. Event-

Related Stress Scale wasn’t standardized so it was classified as follows: 0 = no stress;

1= low stress; 2 = medium stress; 3 = high stress.

We proposed the following classification to define stressful and relaxing events:

Table 1. Classification of relaxing and stressful events.

Value

STRESS

Zscore(STRESS) > 1

Zscore(ARS) > 1

Zscore (SS) > 1

EVS > 1

Zscore(CHALLENGE) & Zscore(SKILLS)

> 1 &

< - 1

RELAX

Zscore(STRESS) < - 1

Zscore(ARS) < - 1

Zscore (SS) < - 1

EVS = 0

Zscore (CHALLENGE) & Zscore (SKILLS)

< - 1 &

> 1

Hierarchical structure of the experiment data makes traditional forms of analysis

unsuitable. Subjects are measured at many time points during each day, across seven

days. Traditional repeated-measures designs require the same number of observations

for each subject and no missing data. Moreover, also other dependencies existing in

the data can be taken into account. Because the ESM entries are nested within seven

days within participants, we estimated the psychophysiological indexes on events

(Relax or Stress), with hierarchical linear analysis, an alternative to multiple

regression suitable for our nested data. We referred to two levels in the model: beep-

level and subject-level. Our model was based on binary logistic, specifying Binomial

as the distribution and Logit (f(x)=log(x / (1−x)) ) as the link function. Using a mixed

hierarchical model we inferred the dichotomised event (Relax or Stress) on the basis

of physiological parameters. In this sense we used these indexes to predict relax or

stress condition indicated by subjects. The analysis aimed at finding statistically

significant parameter for the estimation of a model designed to predict relaxing and

stressful events. More, a linear discriminant analysis (LDA) has been used to verify if

a set of physiological measures (RMSSD, NN50, and HF Power) was able to

discriminate between the two groups (Relax and Stress).

3 Results

The six participants completed a total of 213 ESM reports. Aggregated over

participants’ means, mean Perceived Stress was 2.99 (S.D. = 1.50), mean Activity-

Related Stress was 3.35 (S.D. = 0.72), mean Social Stress was 3.34 (S.D. = 1.40),

mean Challenge was 2.99 (S.D. = 1.92), mean Skills was 4.58 (S.D. = 1.86), and

frequencies for Event-Related Stress was: 88% no stress, 4.2% low stress, 3.1%

medium stress, and 4.7% high stress. A total of 31 events (14.55 % of total events)

have been identified, 18 relax events (8.45 %) and 13 stress events (6.10 %) among

the six subjects. For each one of these events we calculated two temporal HRV

indexes, namely RMSSD and NN50, and one spectral HRV index, i.e. HF power.

In Table 2, means and standard deviations are reported per each index on the basis

of events' group (Relax or Stress). As explained in data analysis, we estimated the

psychophysiological indexes on events (Relax or Stress), with hierarchical logistic

analysis. Results show, a statistical significant hierarchical regression model for

RMSSD (Beta: 1.177; St. Dev.: .5839; p < .044), and a quasi statistical significant for

HF power (Beta: .888; St. Dev.: .4612; p < .055). The RMSSD method is preferred to

NN50 because it has better statistical properties [23].

Table 2. Group Statistics.

Mean Std. Deviation Valid N (listwise)

RELAX

Zscore(RMSSD) .2175527 .78903922 18

Zscore(NN50) .3686125 .80597461 18

Zscore(HF_power) .4263368 .81428263 18

STRESS

Zscore(RMSSD) -.4225023 .73893069 13

Zscore(NN50) -.5293378 .62354657 13

Zscore(HF_power) -.5657602 .57913531 13

TOTAL

Zscore(RMSSD) -.0508575 .82114721 31

Zscore(NN50) -.0079473 .85235392 31

Zscore(HF_power) .0102961 .87036922 31

A linear discriminant analysis (LDA) has been used to verify if the physiological

indexes (RMSSD, NN50, and HF Power) were able to discriminate between the two

groups (Relax and Stress) defined on the basis of the questionnaires, as above defined.

Tests of equality of group means are showed in table 3. More, results showed a 0.622

Wilks' Lambda (Chi-square: 13.070, df: 3, p < .005) with 77.4% of original grouped

cases correctly classified (see table 4).

Table 3. Tests of equality of group means.

Wilks' Lambda F df1 df2 Sig.

Zscore(RMSSD) 0.847 5.233 1 29 0.03

Zscore(NN50) 0.721 11.236 1 29 0.002

Zscore(HF_power) 0.673 14.085 1 29 0.001

Table 4. Classification Results. Overall, 77.4% of original grouped cases correctly classified.

Predicted Group Membership

Condition Relax Stress Total

Original

Relax 72.2 % 27.8 % 100.0 %

Stress 15.4 % 84.6 % 100.0 %

4 Discussions and Conclusions

Recent progress in the sophistication and feasibility of biosensor technology and the

remarkable spread of mobile electronic devices have lead to ubiquitous and

unobtrusive recorder systems that allow naturalistic and multimodal assessment of

psychophysiological parameters [14-16]. Since psychological stress could be defined

as a continuous person-environment transaction [1-3], this integrated and mobile

assessment offers the opportunity to analyze the real-time interaction between

challenges and skills occurring in daily life situations. In this study, we proposed and

tested the use of PsychLog [19] a free open-source mobile experience sampling

platform, aggregated, visualized and collated into reports, to investigate the

fluctuation of subjects’ experience [11] and to detect, on the basis of

psychophysiological real-time assessment, stressful events that normally occur during

daily activities and situations. Analysis has been set selecting two events groups

(Relax and Stress) on the basis of psychological questionnaires. Then, a hierarchical

logistic analysis and a discriminant analysis between the two groups, showed that

physiological measures have been able to predict the groups selected on psychological

basis. These results seem to indicate that a relation between physiological patterns and

psychological behavior exists. Being true these results, we would be able to predict

particular events on physiological basis, i.e. without having to ask subjects about their

own states. Although more psychometric work is needed to validate our innovative

approach, it offers to researchers and clinicians new effective opportunities to assess

and treat psychological stress in daily-life environments. The advantages in using a

mobile psychophysiological stress assessment are potentially several: (a) it is possible

to evaluate the continuous fluctuation of the quality of experience in ecological

contexts; (b) it is possible to schedule the timing and the modality of

psychophysiological monitoring; (c) it allows a multimodal assessment; (d) it permits

the detection of stressful events in daily life; and (e) it provides the opportunity of

giving immediate, graphical and user-friendly feedback. As a consequence of the

detection of a stressful event, PsychLog will be able to give the chance to deliver real-

time and effective Ecological Momentary Interventions [26]; [27] to provide real-time

support in the natural context, when they are most needed.

Acknowledgements

The present work was supported by the European funded project "Interstress” –

Interreality in the management and treatment of stress-related disorders (FP7-

247685).

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