On the Reactivity of Sleep Monitoring with Diaries
M. S. Goelema
1,2
, M. M. Willems
1
, R. Haakma
2
and P. Markopoulos
1
1
Department of Indutrial Design, Eindhoven University of Technology, Eindhoven, The Netherlands
2
Philips Group Innovation Research, Eindhoven, The Netherlands
Keywords: Self-monitoring, Sleep Monitoring, Reactivity, Sleep Diary.
Abstract: The declining costs of wearable sensors have made self-monitoring of sleep related behavior easier for
personal use but also for sleep studies. Several monitor devices come with apps that make use of diary
entries to provide people with an overview of their sleeping habits and give remotely advice. However, it
could be that filling in a sleep diary impacts people’s perception of their sleep or the very behavior that is
being measured. A small-scale field study about the effects of sleep monitoring (keeping a sleep diary) on a
cognitive and a behavioral level is discussed. The method was designed to be as open as possible in order to
focus on the effects of sleep monitoring where participants are not given a goal, motivation or feedback.
Some behavioral modifications were observed, for example, differences in total sleep time and bedtimes
were found (compared to a non-monitoring week and a monitoring week). Nevertheless, what the causes are
of these changes remains unclear, as it turned out that the two actigraph devices used in this study differed
greatly. In addition, some participants became more aware of their sleeping routine, but changing a sleeping
habit was found challenging because of other priorities. It is important to know what the effects may be of
sleep monitoring as the outcomes may already have an effect on the participant behavior which could cause
researchers to work with data that do not represent a real life situation. In addition, the self-monitoring may
serve as an intervention for facilitating healthier sleeping habits.
1 INTRODUCTION
Self-monitoring your sleep is becoming increasingly
accessible to the general public. An already large
number of smartphone applications and dedicated
devices are available on the consumer market that
help track sleep related behavior, (e.g., sleep
duration, waking up during the night, etc.) reflecting
the current trend towards the ‘Quantified Self’ which
seeks to empower individuals to collect information
about themselves, to help them approach health
professionals already informed by an initial analysis,
and to support health interventions with monitoring
their behavior and overall well-being (Swan, 2012).
Actigraphy devices and traditional sleep diaries
are widely used in sleep related research as they are
low cost and allow monitoring sleep behavior in real
life (Carney et al., 2012; Sadeh, 2011). Researchers
have often been concerned with studying
correlations between the two as they provide
subjective and objective measures of sleep quality,
e.g., see (Lockley et al., 1999). However, such
works do not consider the potential effect of self-
monitoring of sleep on a behavioral or cognitive
level. Will people adjust their habits and more
importantly will self-monitoring lead to healthier
habits? Or perhaps it is just knowing that one is
being monitored that makes one feel to adjust his or
her sleeping habits?
Self-monitoring has been researched thoroughly
in the past, especially when it is used as an
intervention, for instance on: weight loss (Butryn et
al., 2007), alcohol consumption (Helzer et al., 2002)
and glucose monitoring (Martin et al., 2006; O’Kane
et al., 2008). However, some of these studies found
an effect of self-monitoring and others did not.
These variations are probably due to methodological
differences or to various levels of subjects’
motivation and predetermined study goals. Still,
self-monitoring on itself could induce adjustments in
behavior. The effect self-monitoring may have on a
cognitive and behavioral level pertains to the
reactivity of self-monitoring. Reactivity is a
phenomenon that emerges when persons alter their
performance or behavior due to the awareness of
being observed (Korotitsch and Nelson-Gray, 1999).
Kazdin (1974) investigated different aspects of self-
240
Goelema, M., Willems, M., Haakma, R. and Markopoulos, P.
On the Reactivity of Sleep Monitoring with Diaries.
DOI: 10.5220/0005662602400247
In Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2016) - Volume 5: HEALTHINF, pages 240-247
ISBN: 978-989-758-170-0
Copyright
c
2016 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
monitoring, such as response desirability, goal
setting and feedback upon people’s performance on
a sentence-construction task. It turned out, amongst
others, that:
1) administering a performance goal or
feedback amplified the reactive effects of self-
monitoring,
2) monitoring one’s own behavior or being
monitored by someone else was equally
reactive,
3) the process of self-recording provoked
behavior change independently of observing
the results.
These findings indicate that the very act of self-
monitoring as such may result in reactive behavior.
(As earlier described in Goelema et al. (2014).)
That reactivity is related with self-monitoring has
been shown in the study of Motl et al. (2012). The
purpose of the study was to test a behavioral
intervention for persons with multiple sclerosis
(MS). However, the average steps per day was
higher during the baseline period compared to the
first week of the behavioral intervention. One
possible explanation for this result may be that
during the baseline period the participants wanted to
make as many steps as possible to make a good
impression. This study is a perfect example of the
need to interpret carefully results pertaining to the
potential reactivity of self-monitoring.
As for the traditional way of sleep monitoring,
keeping a diary, there is little known about the
reactivity effects. Bolger et al. (2003) concluded
that there is insufficient evidence that reactivity
forms a threat to diary validity. Litt et al. (1998)
reported that although their participants became
more aware of the monitored behavior, the self-
monitoring was not reactive. Still, this study only
monitored the urge to drink and not assessed a full
diary. Specifically for sleep monitoring, the effects
of keeping a diary have not yet been investigated.
Only a recent study of Todd and Mullan (2014),
reported that keeping a sleep diary (combined with a
response inhibition intervention) made people avoid
anxiety and stress-provoking activities before going
to bed. However, since the study of Todd and
Mullan includes an intervention, any effects on
behavior may not be attributed to the self-monitoring
per se. Nevertheless, cognitive behavioral therapy is
often supported by keeping a diary and thereby
shaping awareness (Okajima et al., 2011).
We performed a first study (briefly reported in
Goelema et al. (2014)), expecting that tracking
behavior with actigraphy would impact sleep related
behavior. This was not confirmed, and as it turned
out it was the act of keeping a diary that seemed to
impact on the cognitive level though less on a
behavioral level. This finding prompted the current
study where we investigated the effects of keeping a
sleep diary. The set-up of study 1 was reverted for
this study. During the whole three weeks
participants wore an actigraphy device and filled out
only in the second week a sleep diary. We
hypothesized that between the first week of non-
filling out a diary and the week of keeping a diary
the sleep efficiency (SE) and total sleep time (TST)
increases and wake time after sleep onset (WASO)
and bedtime (BT) decreases in the second week.
Secondly, we hypothesized that when comparing
week 2 with week 3 the results would be the reverse,
namely a decrease of SE and TST and increase of
WASO and BT.
2 METHODS
2.1 Participants
To safeguard the reliability of our results, we
decided to recruit participants aged between 40 and
60 years old. The reason for choosing this age range
was that Monk et al. (2003) found a significant
association between lifestyle regularity and good
sleep. Moreover, previous research has shown
irregular sleep quality and rhythm amongst school-
aged children, youngsters, and adolescents (Dahl
and Lewin, 2002; Monk et al., 1994; Sadeh et al.,
2000). To assess whether completing a sleep diary
affects people’s lives, a population is needed that
normally would show regular sleeping times.
Considering this, the target group was set to age 40-
60, assuming that people around this age have the
most stable daily routine and sleep rhythm.
Because of practical constraints relating to the
availability of devices, two groups of 10 subjects
were made, originally resulting in 20 participants for
this study. Participants wore a different device in
each group (see heading measures) and one group
started two weeks later. The process of recruitment
was the same for each group. The intended fifteen
nights of data per participant (total of 300 data
records as opposed to the eventual 203 records) was
reduced greatly because of several circumstances.
These circumstances included failure of data
recording from the devices, failure of setting ‘In and
Out Bed Times’ (software related issues), failure of
following instructions by participants and the
irregularities in the sleep pattern of participants
(emergencies, illness, parties, deadlines, that have
On the Reactivity of Sleep Monitoring with Diaries
241
led to exceptional bed times). The loss of data ruled
out five participants, eventually resulting in a total of
15 participants for this study.
2.2 Measures
Participants were asked to fill in the Pittsburg Sleep
Quality Index (Buysse et al., 1989) to determine
their normal sleeping behavior. The PSQI contains
19 self-rated questions, which are combined to form
seven ‘component’ scores, each ranging from 0-3
points (‘0’ indicating no difficulty and ‘3’ indicating
severe difficulty). The seven component scores
together form a ‘global’ score, ranging from 0-21
points, ‘0’ indicating no difficulty and ‘21
indicating severe difficulties in all areas.
The ‘Consensus Sleep Diary’ (CSD) was used
only in the second week, which contains questions
about initiating and maintaining sleep as well as a
global appreciation of sleep (Table 1) (Carney et al.,
2012) Sleep diaries are effective tools to get an
insight in participants’ sleeping behaviour and
discover changes in sleeping patterns.
Table 1: Consensus Sleep Diary – Core.
Consensus Sleep Diary – Core
1. What time did you get into bed?
2. What time did you try to go to sleep?
3. How long did it take you to fall asleep?
4. How many times did you wake up, not
counting your final awakening?
5. In total, how long did these awakenings
last?
6. What time was your final awakening?
7. What time did you get out of bed for the
day?
8. How would you rate the quality of your
sleep? (Very poor, poor, fair, good,
very good)
At the end of the study, a short interview was held
with each participant. Amongst others they were
asked if and how the CSD influenced their
behaviour. Did they go to bed and get up earlier or
later because of the diary? Did they change any
rituals and were they more aware of the hours of
sleep they should be getting? Furthermore they were
asked about their sleep experience during the whole
period of the study, and more specifically whether
they slept better or worse comparing the different
phases of the study. In addition, questions regarding
the effect of wearing an actigraphy device during
sleep were also assessed. The closing interview also
took care of certain irregularities that might have
occurred in the data (e.g., occasions that might have
disturbed a good night’s sleep, or events that
required the participant to go to bed much later or
get up much earlier than regularly). Lastly, during
the closing interview we revealed to them the actual
goal of the study (investigating the reactivity effects
of a sleep diary).
Participants in Group 1 (participants 101-107)
were given the Philips Actiwatch Spectrum (Philips
Respironics, Inc, Murrysville, USA), while
participants in Group 2 (participants 108-115) were
given the ActiGraph device (ActiGraph GT3X,
LLC, Pensacola, FL). Both devices make use of a
accelerometer to detect and log wrist movement,
also known as actigraphy. The Actiwatch Spectrum
contains a piezoelectric accelerometer with a
sensitivity of 0.025g. The hardware of the Actigraph
consists of a triaxial accelerometer with a sensitivity
of 0.05 g. They were set to a standard data sampling
rate of 120 per hr (every 30 seconds), providing
ample data per night. In addition, both devices have
an ambient light sensor but these outcomes were not
used in this study.
2.3 Procedure
The participants were instructed to wear an
actigraphy device on their non-dominant hand from
Sunday-Monday night to Thursday-Friday night for
3 weeks straight, excluding the weekends and
including the wake-up-times on working days.
Participants were asked to fill out the CSD each
morning, only during the second week.
Many actigraphy sleep–wake scoring algorithms
rely on sleep diary information to set scoring periods
for sleep onset and offset. In this study, participants
were instructed to start wearing their device when
they were trying to fall asleep. In case of going to
bed earlier to read a book, watch television, etc. they
were told not to wear the actigraphy device until
they actually wanted to go to sleep and then to put it
on the wrist. Furthermore they were instructed to
take off the actigraphy device right after their final
awakening. For the Actiwatch the same procedure
was applied, only the function of the marker button
to indicate that someone wanted to fall asleep was
explained extra, but eventually barely used by the
participants.
2.4 Data Analysis
‘In Bed’ and ‘Out of Bed’ times were determined by
analyzing the beginning and endings of the activity
graphs. One participant reported to have forgotten to
take the ActiGraph off. This omission and other
HEALTHINF 2016 - 9th International Conference on Health Informatics
242
mistakes were corrected by using the ‘In Bed’ and
‘Out of Bed’ times indicated in the diaries. In case of
unreliable indications, data was removed from the
calculations. The sleep efficiency was calculated as
ratio of the total minutes of sleep time (TST) divided
by the total minutes of time in bed (TTB).
Statistical analyses were conducted using SPSS
IBM 20. New variables were computed to extract the
mean of the SE, WASO, TST and BT of each week
to take care of missing values. Repeated measure
analysis was conducted, with time (the phases of the
experimental study) as the within subject factor. The
contrast repeated was used, to compare week 1 with
week 2 and week 2 with week 3. The assumption of
sphericity was met, meaning that the level of
dependence between experimental conditions was
roughly equal. However, the assumption of
normality was not met and therefore the outcome of
the Greenhouse-Geisser test was used. The repeated
measures analyses were done for each parameter
(SE, TST, WASO and BT), and for the complete
group. First, we looked at whether there was a main
effect for time and if so, then pairwise comparisons
were examined. For each group separately paired-
sample t-test or Wilcoxon signed ranked test was
conducted. Statistical significance was set at p
<0.05.
3 RESULTS
The characteristics of the sample are listed in Table
2. The average age of the sample was just under 52
years and 10 out of 15 were women. For group 1, the
average amount of sleep time was 5.89 hours (SD
=0.9 hours) and for group 2 it was 6.55 hours (SD =
0.82 hours). The average PSQI score was a little
above the cut-off score of 5, this would indicate that
the participants experienced slight sleeping
problems.
Table 2: Demographic data and sleep characteristics of the
samples.
Group 1
(N = 7)
Group 2
(N = 8)
A
g
e 51.14 (5.8) 50.9 (3.9)
Gender 5 5
Bedtime 24:05:49 (41:13) 23:25:29 (36:51)
TST 353.23 (54.3) 393 (48.9)
WASO 58.11 (23.2) 36.9 (20.6)
SE % 81.5 (6.4) 91.3 (4.7)
PSQI 6 (2.5) 5.7 (2.4)
Note. Values are mean (standard deviations) or percentage of cases.
Bedtime = (hh:mm:ss)/ (mm:ss)TST = total sleep time (min), WASO =
Wake time after sleep onset (min), SE = Sleep efficiency and PSQI =
Pittsburgh sleep quality index.
First all data from both groups were analysed
using the mean value for each week per participant.
For the complete group no significant results were
found, between baseline and week 2 or week 2 and
week 3 (for example: SE, mean week 1 = 85%,
mean week 2 = 87%; df = 2, F = 2.54, p = .097,
Figure 1).
Figure 1: Mean sleep efficiency (SE) for each week,
displayed for each group separately and for the whole
sample, including error bars (1 +- SD).
When testing the hypotheses for only the
participants who wore the Actiwatch no significant
results were found for SE and WASO. There was a
significant difference between baseline and week 2
for TST (Z = -2.197, p = .028; mean TST: week 1 =
328, week 2 = 361, Figure 2). This means that
during the week of filling out the sleep diary the
total sleep time was longer than during baseline.
Moreover, a difference in mean bedtimes was
observed between week 2 and week 3 (Z = -2.366, p
= .018; mean BT: week 2 = 24:09:14 week 3 =
23:47:27, Figure 3). This indicates that in the last
week of the study participants went to bed earlier
than during week 2.
For the ActiGraph group no significant results
were observed between baseline and week 2 or week
2 and week (For example: WASO, Z = -,98, p =
.327; mean week 2 = 35,74, mean week 3 = 36,91,
Figure 4).
3.1 Closing Interview
The closing interview revealed that it was difficult
for the participants to notice any difference in sleep
quality on a weekly basis. The participants did not
change their sleep routine based on filling the
diaries. They did not go to bed earlier or changed
their alarm clock settings. However, a handful of
participants indicated that filling in the diary made
them more aware of their sleeping habits.
On the Reactivity of Sleep Monitoring with Diaries
243
Figure 2: Mean total sleep time for each week, displayed
for each group separately and for the whole sample,
including error bars (1 +- SD).
Figure 3: Mean bedtimes for each week, displayed for
each group separately and for the whole sample, including
error bars (1 +- SD).
Figure 4: Mean wake time after sleep onset (WASO) for
each week, displayed for each group separately and for the
whole sample, including error bars (1 +- SD).
Five participants reported that they did have to
get used to the actigraphy devices during the first
few days of the experiment. They indicated that this
might have had a minor influence when falling a
sleep the first few days. Except for one participant
who reported very bad sleep during phase 2,
according to that subject of wearing the actigraphy
device. What did become apparent was that as the
study evolved wearing the actigraphy devices
became an automated process of which the
participants were less aware than in the beginning.
4 DISCUSSION
A significant effect was found when comparing TST
of week 1 with week 2 and between bedtimes of
week 2 and week 3 of the Actiwatch group. The
increase of the TST was expected while the decrease
of bedtimes in week 3 was not anticipated, as we had
expected the opposite effect. It could be that the
decrease in bedtimes in week 3 is caused by keeping
the diary which may have had longer lasting effects
(the week after). No significant results were found
with the overall group or the Actigraph device.
Because of trends seen in the graphs, we also
compared the baseline with week 3, and found
significant effects between total sleep time and
bedtimes in the Actiwatch group, however, what the
cause of these effects could be remains unclear.
Participants indicated during the closing interview,
that they experienced worse sleep only in the first
one or two nights of the study, because they had to
get used to the actigraphy device and the general
idea of participating in a sleep related study.
Moreover, no differences were found in the
outcomes between good and bad sleepers based on
PSQI scores ( 5 is considered as good).
In this study important differences were found
between the results of the Actigraph and the
Actiwatch devices. The reliability and validation of
actigraphy is a much discussed topic and it may have
played a role in this outcome. Related reports
support the validity of data recording with the
Philips Actiwatch (Gironda et al., 2007; Hyde et al.,
2007; Weiss et al., 2010), whereas the Actigraph
seems to be less reliable (Hjorth et al., 2012). The
golden standard for sleep studies is considered to be
polysomnography (PSG). Although actigraphy and
PSG tend to correspond reasonably well (Ancoli-
Israel et al., 2003; Tryon, 2004), research reports
measurements errors in actigraphy (Blackwell et al.,
2008). The accuracy of the Actiwatch and Actigraph
is respectively 86.3% (measured in young and older
adults, healthy or chronic primary insomniac and 23
night-workers) and 82.8% (based on naps in healthy
young adults) (Cellini et al., 2013; Marino et al.,
HEALTHINF 2016 - 9th International Conference on Health Informatics
244
2013). This discrepancy in accuracy and especially
taking into account the study samples may be a
reason why we found these differences between the
devices. (We found significant differences between
the Actiwatch group and the Actigraph group, for
instance, mean TST week 1, Z = -2.32 p < .021.) In
addition, the differences in accuracy could be due to
different sensor sensitivity, a different way to
calculate activity counts and each have their own
algorithm for sleep/wake detection. As a result,
conclusions based on the objective measures
(actigraphy) of this data set could not be made.
However, due to different populations in the groups,
the differences we found could also have been a
result of between subject variation.
Based on the closing interview, participants
mentioned they did not change any sleeping habits,
but some of them were more aware of their sleeping
routines because of filling in the sleep diary. This
confirms the findings by Goelema et al. (2014)),
where some participants did change or tried to
change their pre-bedtime rituals, probably caused by
filling in the sleep diary. These studies suggest that
presumably motivation or a significant alteration in
the daily routine of a person is a necessary
prerequisite for influencing sleeping behaviour
through self-monitoring.
A remarkable side-note on the failure of
following instructions by participants, is that during
the diary week, hardly any missing data was
recorded while in week 1 and week 3 there was
considerably more missing data. This confirms
earlier investigations on wrist actigraphy adherence
research by Carney et al. (2004), who suggests that
combining actigraphy monitoring with diaries can
increase the likelihood of adherence to sleep
instructions.
The behavior of sleep is deeply rooted in one's
daily routine and modifying this behavior will have
a large impact on the rest of the daily rhythm. Vice
versa, ‘other factors’ influence sleeping behavior
greatly. This means that probably one needs to be
motivated to actually adjust a sleep behavior. When
participants are motivated for adjusting a behavior,
in the majority of studies, significant results have
been found, at least for the short-term (Bouffard-
Bouchard et al., 1991; Zimmerman and Kitsantas,
1999). Although these studies were all conducted on
different topics, such as improving learning skills
than on changing sleeping habits, there is a high
likelihood that the same will be true for adjusting a
sleeping behavior or thought. This would mean for
sleep monitoring, that when individuals are
motivated they are more eager to adjust their
sleeping behavior and this could lead to alterations
in the daily routine of that person. Moreover, it will
increase the level of self-control and could
contribute to a healthier lifestyle, as it becomes more
known that sleeping well is essential for health,
psychological well-being and daytime functioning
(Totterdell et al., 1994).
The importance of motivation for self-monitoring
can be integrated into a theory of self-regulation,
however several versions of the self-regulation
theory are proposed (Ajzen, 1991; Fishbein, 1979).
Schunk and Zimmerman (2008) argue that
motivation is an essential dimension of self-
regulation learning, while other theories put more
emphasis on the self-efficacy beliefs and
discrepancy in costs and benefit it may have on the
short or long term.
In addition, this motivation may be affected by
the feedback a monitoring device gives. When the
insight into a certain behavior increases, this will
make a person more aware of their behavior and
therefore the feedback could turn into an agreeable
argument to get motivated to adjust that behavior. If
feedback could have been given immediately then
the outcome might have been different, as has been
found in several other studies (Gajar et al., 1984;
Kazdin, 1974). Most consumer-level devices
available in the market supply feedback to their
users, giving users a great insight in their monitored
behavior. Moreover, persons are encouraged to set
personal goals, to acquire a healthier lifestyle. For
persons with already a high desire of self-control
this would serve as a handle to gain control of one’s
life.
This study is the first study that makes an
attempt to explore the reactivity of sleep measures in
a quantitative way. The effect size found of the
significant result between TST 1 and TST 2 for the
Actiwatch group was medium: .587. However,
whether this effect size is significant for clinical
purposes remains unknown. On average a person
sleeps between 7 and 8 hours, but what the
acceptable deviation from 7 a 8 hours is, is not
known. There is no clear clinically relevant effect
size within the sleep field operationalized.
Moreover, an individual situation is probably more
important for disparity in total sleep time, as sleep is
very interlinked with the daily routine it can have a
significant effect for one individual and not for
another individual.
As mentioned in the method section, there was a
difference in the expected 300 data records and the
eventual 203 data records, because of unforeseen
circumstances. The loss of data should be accounted
On the Reactivity of Sleep Monitoring with Diaries
245
for and possibly be prevented. Future research, could
attempt to replicate these results during a longer
study. To adjust behavior, and especially (sleep)
behavior that is incorporated in the daily routine, it
probably takes more time to adjust. In addition, a
cross-over design or adding a control group that is
not subjected to a diary is another way to control and
justify temporal effects. Moreover, a distinction
between persons who already want to improve their
sleep and those who are just curious about their
sleep should be made. This will give an insight into
the motivation level of a participant before starting
with the study, which is an important factor on their
thoughts and behavior. Lastly, the influence of
feedback should also be accounted for by the study
method, to find out what the effects are of feedback
on sleep monitoring.
To conclude, objective behavioral changes were
observed in the Actiwach group whether this is due
to the device that was used or a real behavior change
remains inconclusive. Nevertheless, higher
awareness due to filling in the diary was observed in
both of our studies. It is important to know what the
effects can be when self-monitoring your sleep, as
the prospect is that monitoring physiological
features will become more and more normal and
more advanced devices will be available. This can
lead to more awareness of the behavior that is being
monitored. Moreover, the effects of sleep
monitoring need to be taken into consideration when
someone is coached remotely, as data that is
presented may not represent real life information.
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