Using WatchMinder to Increase the On-Task Behavior of Students with Autism Spectrum Disorder

Abstract

This study assessed the use of WatchMinder™, a vibrating prompt watch, and self-graphing on the on-task behavior of students with autism spectrum disorder in an elementary special education setting. Using a multiple baseline across subjects design, results showed an immediate increase in on-task behavior when the intervention was introduced. Participants maintained high levels of on-task behavior during the follow-up phase. Implications for expanded self-monitoring treatment packages are discussed.

Full text

ORIGINAL PAPER
Using WatchMinder to Increase the On-Task Behavior
of Students with Autism Spectrum Disorder
Lisa Finn •Rangasamy Ramasamy •
Charles Dukes •John Scott
ÓSpringer Science+Business Media New York 2014
Abstract This study assessed the use of WatchMinder
TM
,
a vibrating prompt watch, and self-graphing on the on-task
behavior of students with autism spectrum disorder in an
elementary special education setting. Using a multiple
baseline across subjects design, results showed an immedi-
ate increase in on-task behavior when the intervention was
introduced. Participants maintained high levels of on-task
behavior during the follow-up phase. Implications for
expanded self-monitoring treatment packages are discussed.
Keywords Autism spectrum disorder Self-monitoring 
Tactile prompting WatchMinder Self-graphing 
Classroom intervention
Introduction
Individuals with autism spectrum disorder (ASD) often
struggle with focusing and sustaining attention, rapidly
retrieving relevant information, managing time effectively,
self-monitoring, self-correcting, and sequencing a plan of
action (Bjorklund 2012). These skills, collectively known
as executive functions, are coordinated in the brain and
work together to help a person achieve goals. Executive
functioning skills develop naturally in most individuals
without disabilities. However, those with ASD may require
systematic interventions to acquire such complex skills.
‘‘In the academic setting, the ability to attend to tasks is a
requisite skill for success in school,’’ (Holifield et al. 2010,
p. 230). Therefore, individuals with attention challenges,
such as those with ASD or attention deficit hyperactivity
disorder, may benefit from self-monitoring interventions
that target these specific executive functioning skills
(Milley and Machalicek 2012). Self-monitoring is a covert
process involving self-assessment and self-recording
enabling an individual to become more aware of whether
he/she is performing a specific task (McDougall et al.
2012). These skills are critical for many daily life tasks,
including time management, acquiring and comprehending
new information, meeting deadlines or due dates, and
performing multi-step tasks (Lee et al. 2007).
Self-monitoring training is a proactive intervention that
can be individualized and applied in a variety of settings.
There have been numerous studies that support self-moni-
toring as an effective practice in the field of education. For
example, in a review of literature, Anderson and Wheldall
(2004) analyzed 44 research studies on self-monitoring
between 1991 and 2003 and concluded that self-monitoring
was effective in helping students increase their attention and
on-task behavior. Positive effects of self-monitoring have
been reported in the literature on a range of target behaviors
for individuals with a variety of disabilities and across
several age groups, including high school students with
multiple disciplinary referrals (Blick and Test 1987), adults
with traumatic brain injury (Van Hulle and Hux 2006),
adults with intellectual disabilities (Green et al. 2011), and
school age students with developmental disabilities and
learning disabilities (Amato-Zech et al. 2006;Milleretal.
2007; Trammel et al. 1994). In addition, many studies have
focused on the effectiveness of self-monitoring programs
specifically for students with ASD. Such research targeted
skills including increasing on-task behavior (Callahan and
Rademacher 1999;Holifieldetal.2010; Legge et al. 2010),
reducing self-stimulatory behaviors (Koegel and Koegel
L. Finn (&)R. Ramasamy C. Dukes J. Scott
Department of Exceptional Student Education, College
of Education, Florida Atlantic University, 777 Glades Rd.,
Boca Raton, FL 33431, USA
e-mail: lisa.finn@palmbeachschools.org
123
J Autism Dev Disord
DOI 10.1007/s10803-014-2300-x

1990; Mancina et al. 2000), increasing independence in
social settings (Parker and Kamps 2011), and increasing
academic productivity (Farrell and McDougall 2008;Soares
et al. 2009).
Self-monitoring interventions typically do not stand
alone since it is a complex skill to teach and requires many
processes to work together simultaneously. Therefore,
many interventions intended to promote self-monitoring
are delivered as ‘‘treatment packages’’ comprised of sev-
eral components including self-recording, goal setting,
evaluation, graphing/charting, and reinforcement (Briesch
and Chafouleas 2009). In addition, these packages typically
include prompts such as a bell or recorded tone (Callahan
and Rademacher 1999; Holifield et al. 2010; Koegel and
Koegel 1990; Mancina et al. 2000; Parker and Kamps
2011). While these prompts are effective in training stu-
dents to self-monitor, they have limitations because of their
obtrusiveness, and possible interference with generaliza-
tion (Amato-Zech et al. 2006; Anson et al. 2008). A tactile
prompting device may hold special advantages over audi-
tory cues in that they can be more discreet and easily set to
deliver the prompt in accordance with the unique needs of
the individual (Anson et al. 2008; Legge et al. 2010). Such
advantages make using tactile prompting devices particu-
larly feasible for inclusive educational settings. Further-
more, tactile prompting devices are portable so they may
be more practical for facilitating generalization and spon-
taneous use of acquired skills (Farrell and McDougall
2008; Lee et al. 2007).
Currently, there are several types of tactile prompting
devices available. WatchMinder, a vibrating wristwatch, is
one tactile prompting device. Van Hulle and Hux (2006)
successfully used WatchMinder to teach adults with trau-
matic brain injuries to remember to take their medications.
Green et al. (2011) used WatchMinder to assist adults with
intellectual disabilities with task completion and transition
skills within the workplace. Another tactile prompting tool
called MotivAider is a pager-like device that clips to the
waistband and is used to provide vibrating prompts so
students can monitor themselves (Richards et al. 2014).
Farrell and McDougall (2008) utilized MotivAider to
increase math fact fluency for high school students with
disabilities by helping them self-monitor their work pace.
Legge et al. (2010) successfully used MotivAider to help
three-fifth and sixth grade students to increase their on-task
behavior during math and the students were able to main-
tain their on-task behavior once the MotivAider was
removed.
It is essential that individuals become active participants
in their self-monitoring programs because the nature of the
skill requires self-directed behavior (Briesch and Chafou-
leas 2009). Involving students as active participants in all
steps of the intervention process is important because it can
help them learn to set goals, make plans, identify struggles,
and ideally evaluate their own progress (Sebag 2010).
Additionally, self-graphing has the potential to improve
motivation because it provides immediate feedback
(Anderson and Wheldall 2004). Few studies have included
techniques to increase active participation. Although many
researchers have recommended goal setting and self-
graphing as potentially effective components of self-mon-
itoring programs (i.e., Anderson and Wheldall 2004; Bri-
esch and Chafouleas 2009; Sebag 2010), in a meta-
analysis, Joseph and Eveleigh (2011) reported that only
five of the 16 studies reviewed included a self-graphing
component. Therefore, there is a need for more research on
the newest technologies available for self-monitoring pro-
grams including tactile devices and student friendly
graphing applications. The purpose of this study was to
evaluate the (a) efficacy of WatchMinder as a means to
promote self-monitoring to increase on-task behavior,
(b) effects of self-graphing using an iPad application, and
(c) maintenance of self-monitoring skills when the inter-
vention was removed.
Methods
Participants and Setting
Four students participated in the study. They all received
special education services in a community elementary
school in South Florida. Pseudonyms are used in place of
students’ names. See Table 1for demographic information.
Adam was 8 years 10 months old at the time of the
study and was in third grade. Adam was working on grade
level curriculum for all academic subjects. However, he
required frequent verbal reminders to remain focused and
engaged in the task. He had difficulty completing work
independently due to prompt dependency and often was not
aware of his off-task behavior.
Bill was 8 years 7 months old and was in third grade.
During the study, Bill was performing on grade level for all
academic subjects. He had difficulty following directions,
adhering to the classroom routine, and initiating and
completing tasks. His preoccupation with imaginary
games, guns, and violence contributed to his off-task
behavior during independent seatwork.
Paul was 9 years 10 months old and was in fourth grade.
Paul’s academic skills were about 2 years below his
chronological age. Paul was reliant on prompts to complete
work independently. He frequently left his work area,
played with his materials, and laid his head on his desk
during seatwork. He often demonstrated problem behaviors
including screaming, lying on the floor, and talking to
himself.
J Autism Dev Disord
123

Tom was 8 years 8 months old and was in third grade.
He was on-grade level for all subject areas. However, he
demonstrated compulsive tendencies that caused him dif-
ficulty with completing tasks. For example, he spent most
of his time making sure his answers were sized to fit
exactly on the line given on the worksheet. He was also
highly distracted by other’s activities in the classroom and
often watched the teacher working with other students
rather than working on his tasks.
Students were chosen to participate in this study if they
were diagnosed with ASD and participated in the autism
cluster program for at least a portion of the school day. All
of the students were highly distracted while working on
academic tasks and required numerous verbal prompts. In
addition to these characteristics, each of the participants
were suitable for a self-monitoring intervention because
they were able to differentiate between working and non-
working behavior and were capable of completing some
tasks within the classroom independently.
The study was conducted in the first author’s classroom.
A total of ten students in the class followed individual
schedules to complete a rotation of activities including
language therapy, small group instruction, and hands on
tasks. In addition, each student completed a 30-min inde-
pendent work period at some point during the day at stand-
alone desks located in the middle of the classroom. This
period served as the intervention period for the four par-
ticipants in this study.
Experimental Design
A multiple baseline across participants design was used to
teach self-monitoring skills. This design is used to analyze
the effect of an independent variable across several par-
ticipants so that the variable’s function can be predicted by
the level of change in the participant who receives the
intervention while little or no change is evident with those
who have not yet received the intervention (Richards et al.
2014). In this study, the independent variable self-moni-
toring intervention package was used to measure the per-
centage of on-task behavior during daily independent work
sessions.
Task and Behavioral Measures
The dependent variable in this study was on-task behavior.
On-task behavior was defined for each student based on the
actions required during the work period. A participant was
considered to be on-task if he was demonstrating any of the
behaviors listed on his checklist as ‘‘working’’ when the
WatchMinder vibrated. On and off-task were operationally
Table 1 Demographic
characteristics of participants
IQ’s were obtained from
different evaluations (Bill-
WISC-IV; Paul- DAS-2; Tom-
Leiter-R). No IQ score was
available in Adam’s file
LI Language impairment, OHI
otherwise health impaired, IQ
intelligence quotient
Participant Age Grade Ethnicity Educational
eligibility
IQ Academic services
ASD Gen. Ed.
Adam 8:10 3 Caucasian ASD, LI N/A Reading
Writing
Math
Science
Social studies
Bill 8:7 3 Caucasian ASD, OHI 97 Reading
Writing
Math
Science
Social studies
Paul 9:10 4 Hispanic ASD, LI 71 All Academics
Tom 8:8 3 Hispanic ASD, LI 101 Reading
Writing
Science
Social studies
Math
Table 2 On- and off-task definitions across participants
Participant On-task Off-task
Adam Reading
Writing the answers
Raising hand for help
Putting work in finished
basket
Looking around the
room
Staring at paper
Rolling pencil on desk
Calling out
Bill Reading
Writing the answers
Raising hand for help
Putting work in finished
basket
Looking around the
room
Drawing pictures on
work
Staring at paper
Paul Writing
Cutting
Gluing
Out of seat
Making noise
Looking around the
room
Playing with materials
Laying head on desk
Tom Reading
Writing the answers
Raising hand for help
Putting work in finished
basket
Looking around the
room
Looking at the teacher
Staring at paper
J Autism Dev Disord
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defined for all participants so they could learn to differ-
entiate between the two when they were taught to self-
monitor. See Table 2for on and off-task behavior defini-
tions for each participant.
The independent variable in this study was the self-
monitoring intervention package that included the Watch-
Minder, a checklist, self-graphing using Data Manager Pro,
and a reinforcer. Some of the common reinforcers were
playing the Angry Birds board game, extra computer time,
playing MineCraft on the iPad, and drawing time.
Data Collection
Data collection took place during a 30-min independent
work period while the participants were working on pre-
viously mastered academic tasks at stand-alone desks
located in the central part of the classroom. Data were
collected by the first author and the classroom parapro-
fessional across conditions and by participants during
intervention phases using a momentary time sampling
system. When the WatchMinder vibrated, it cued the stu-
dent to assess what he was doing at that moment and then
record it on his checklist. During the training, for self-
monitoring and self-monitoring plus graphing phases, the
watch was set to a 2-min fixed interval and it displayed the
message ‘‘PAY ATTN’’ (pay attention) when it vibrated.
During the fading and maintenance phases the watch was
set to a 5-min fixed interval. In addition, adult observers
collected data by wearing WatchMinders using identical
settings.
Materials
The materials used in this study included the WatchMinder,
adult and student checklists, an iPad with the Data Man-
ager Pro application, and student-selected reinforcers. The
WatchMinder is a vibrating prompt watch that resembles a
digital sport watch. The device is available in black or
white and can display as many as 65 preprogrammed
messages including ‘‘use the bathroom, pay attention,
relax, eat, and take medication.’’ The watch can be set to a
fixed interval from one to 60 min that automatically repeats
for the selected duration. Each morning before the students
arrived in class the first author calibrated all of the
WatchMinders to ensure they would vibrate at the exact
same time.
In addition to the WatchMinder, each participant was
given a checklist on a half sheet of 8911 in. white
paper. The checklist defined specific behaviors that were
considered to be ‘‘working’’ (on-task) and ‘‘not working’’
(off-task). Participants used the checklist to self-record
when the watch cued them. Adult observers used a data
sheet with columns to collect five sessions worth of data for
each participant.
Data Manager Pro is a graphing application for iPhone
and iPad. It allows for multiple data files to be created on
the home screen. Each participant was assigned a file based
on a participant number in the first author’s iPad to
maintain confidentiality. After tapping on their assigned
file, an input screen allowed for data to be entered at the
completion of each work session. At the bottom of the
input screen there was an option to look at a line graph of
the data. There was also an option to set a goal line, which
will place a red line across the graph.
Procedure
Baseline
During baseline, the participants were observed to measure
the percentage of intervals they were on-task during the
independent work period without the use of WatchMinder
or a checklist. They were expected to sit at their desks and
complete the assigned work located in their independent
workbaskets. Students received verbal prompts for redi-
rection when needed. The first author and a paraprofes-
sional wore WatchMinders set at 2-min fixed intervals and
began data collection once the student retrieved his mate-
rials and began working. The first participant entered the
training phase after three stable baseline sessions. The
remaining participants moved from baseline to intervention
once previous participant demonstrated at least three con-
secutive sessions of 100 % accuracy in self-recording
during the training phase, and when the next target par-
ticipant had at least four stable baseline data points. An
exception to the baseline criteria was made for Tom.
Although his baseline data was unstable, the decision was
made to intervene for clinical benefit rather than scientific
research purposes in hopes that the intervention would help
him perform well on a more consistent basis.
Training
The WatchMinder, as well as the training procedure, was
introduced to each participant. Training included an
11-step procedure that involved systematically fading
verbal prompts and increasing proximity from the partici-
pant while he monitored himself and completed work tasks
(see Table 3). During the work session when the watch
vibrated, the participant was taught to ask himself, ‘‘What
am I doing right now?’’ and then check ‘‘yes’’ or ‘‘no’’
accordingly on the checklist. For example, on the first day
of training, each participant was asked to verbalize what he
was doing each time the watch vibrated and mark that
behavior on his checklist accordingly while the adult stood
J Autism Dev Disord
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next to him. In subsequent days, as the participant was able
to accurately identify what he was doing when the watch
vibrated, the requirement to verbalize the behavior was
faded and the adult observer monitored the student from
across the room. As each participant demonstrated profi-
ciency with completing a training step for two consecutive
sessions, instruction was no longer given on that step. The
criterion for moving to the next phase was five consecutive
sessions of 100 % accurate recording, regardless of the
percentage of time on-task.
Self-monitoring
The participants wore the WatchMinder and independently
completed the same steps that were taught during the
training procedure. The watch was set to a 2-min interval. At
the end of the 30-min session, the participants independently
counted their ‘‘yes’’ checks, placed their checklists and
watches in the correct location, and retrieved the reinforcer
if it was earned. Participants earned their reinforcer if they
were on-task for at least 13 of the 15 intervals. The criterion
for moving to the next phase was five consecutive sessions
of at least 80 % or more intervals on-task. After becoming
proficient with using the WatchMinder to self-monitor, self-
graphing was added to the intervention.
Self-monitoring Plus Graphing
The purpose of self-monitoring plus graphing was to assess
whether adding a graphing component to the intervention
package would contribute to an increase in on-task
behavior compared to only using the WatchMinder and a
checklist. All procedures during the self-monitoring plus
graphing phase were the same as those in the self-
monitoring phase. However, at the end of the session, the
participants were trained to graph their data point on the
Data Manager Pro application. Training involved showing
participants how to convert the number of intervals mea-
sured on-task into a percentage by looking at a percentage
chart posted on the inside of the cabinet where the watches
were stored. Then they were taught to tap the application,
access their data file, and input the percent of intervals on-
task. After inputting the data, the participant was able to
view a graph by taping ‘‘graph’’ at the bottom of the iPad
screen. Prior to participants entering this phase, participant
data was entered into the application so they would be able
to compare their current progress with what they had done
previously. This application also allows a goal line to be
put into the graph. This was set at 80 % for each participant
since the criterion for reinforcement during each session
was 80 % or more intervals on-task. The participants
moved onto the next phase in the study after five consec-
utive sessions of 80 % or more responding.
Fading
The purpose of the fading phase was to decrease the
amount of feedback participants received from the watch
with the hypothesis that they would be able to maintain
high levels of task engagement without the watch vibrating
as often. During this phase, all procedures were the same as
the previous phase, including the graphing, except the
WatchMinders were set to a 5-min fixed interval rather
than 2-min. As with other phases, the participants met
criteria for this phase if they demonstrated five consecutive
sessions of on-task behavior for 80 % or more intervals.
Follow-Up
Behavior was measured during this phase, as the ultimate
goal in teaching students to self-monitor, so that they will
be able to engage in certain behaviors without prompting of
any kind (Wilkinson 2008). It was important that the
WatchMinder was faded as soon as possible to avoid
prompt dependency. Therefore, after participants met cri-
teria with the watch being set to a longer interval, the
WatchMinder and checklist were removed. Adult observers
continued to keep data on each participant’s on-task
behavior for five consecutive sessions following the
removal of the WatchMinder. Two additional probes were
collected 1 week apart beginning 1 week after the five
consecutive follow-up sessions.
Interobserver Agreement and Intervention Fidelity
The first author and the classroom paraprofessional com-
pleted interobserver agreement (IOA) across all conditions.
Table 3 Eleven-step training procedure
1. Show student how to get checklist and watch from the closet
2. Ask the student to write the date and what he is working for on
the checklist
3. Review the criteria for ‘‘on’’ and ‘‘off-task’’ on the checklist
4. Ask the student to verbalize what he was doing when the watch
vibrated and tell him to check yes or no accordingly
5. Instruct the student to shade in the check box when the reminder
vibration occurs
6. Watch the student as he records with decreasing proximity
7. Intervene if the student inaccurately records for two consecutive
intervals
8. Instruct student to count yes checks at the end of the session
9. Discuss accuracy of recording with the student
10. Show student where to put the watch and checklist at the end
of the session
11. Provide reinforcement if earned according to the requirements
on the student checklist
J Autism Dev Disord
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Prior to collecting IOA data, the paraprofessional was
trained and practice sessions were conducted until both
observers agreed for 100 % of intervals for each partici-
pant. Data were collected using the same participant watch
settings: 2-min for baseline, training, self-monitoring, and
self-monitoring plus graphing, and 5-min for fading, and
follow-up. IOA was completed for approximately 40 % of
sessions for each participant. It was calculated by dividing
the number of agreements by the number of agreements
plus disagreements. The mean agreement on the dependent
variable across participants and phases was 95.3 %
(67–100 %). Three sessions during baseline resulted an
IOA of 67 %. Also, during the fading phase there were two
sessions in which IOA was at 67 % due to two disagree-
ments between observers. However when that occurred,
both data collectors reviewed the definitions of on-task
before collecting subsequent data.
To maintain fidelity of the intervention, the paraprofes-
sional collected data on the first author as she implemented
the WatchMinder intervention program with two of the four
participants due to her availability. A checklist containing
the 11-step training procedure was used for the first six
sessions of training for both Paul and Tom, which consisted
of 60 % of the training sessions. A step was marked as not
applicable if the student was able to complete the step
independently during the two previous sessions. For exam-
ple, once the participant was able to retrieve the watch and
checklist from the cabinet on his own, the researcher did not
continue to show him how during subsequent sessions.
Treatment fidelity data was calculated by dividing the
number of steps the observer saw the trainer implement by
the total number of steps in the training procedure multiplied
by 100. Using this formula, 100 % percent of the steps in the
training procedure were completed accurately.
B
Fig. 1 Self-monitoring
intervention across participants.
Note.BL=baseline,
SM =self-monitoring,
SM ?G = self-monitoring plus
graphing, F =fading,
F2 =fading 2, F3 =fading 3,
B=booster session,
FU =follow up. * =Session
break due to prolonged illness.
Adam was the only participant
to receive the F2 and F3 phases
J Autism Dev Disord
123

Results
Figure 1displays the effectiveness of WatchMinder on
self-monitoring the on-task behavior of all four partici-
pants. The results reported in Fig. 1include the data col-
lected by the first author in the baseline and training phase,
and participant data once they were responsible for col-
lecting their own data during intervention phases.
The first panel of the graph in Fig. 1displays Adam’s
results. Adam’s baseline data revealed a mean percentage
on-task of 23.5 % and a decelerating trend. When Adam
entered the training phase his level of on-task behavior
increased immediately and the mean for his on-task
behavior was 86.15 %. His on-task behavior remained at
very high levels during the self-monitoring and self-mon-
itoring plus graphing phases (93.9 and 92.2 %, respec-
tively). During the fading phase, Adam’s mean percentage
on-task was 93.2 %. As he entered the follow-up phase, he
became very ill with the flu and was absent on and off for
the next 5 weeks, a total of 24 school days. Increased
variability during his cycles of illness created a need for
additional fading phases and booster sessions as depicted in
Fig. 1. Once Adam’s health improved and he was in school
consistently, his performance stabilized and returned to
levels achieved previous to illness. During the Fading
phase 3, his mean percentage of on-task behavior was
86.4 %. In the final follow-up phase, his mean percentage
on-task decreased slightly to 76.2 %.
The results for Bill are shown in the second panel of the
graph in Fig. 1. Baseline data for his on-task behavior
revealed a mean of 21.5 %. A trend line using the split-
middle method (Gast 2010) revealed a slight acceleration.
However, since the level of his performance was very low
(a median level of 20), the intervention was implemented.
When the training was introduced, the level of Bill’s on-
task behavior increased immediately and remained above
80 % for the rest of the intervention. Bill’s mean per-
centage of on-task behavior in the training phase was
94.5 %, self-monitoring 96 %, self-monitoring plus
graphing 97.2 %, and fading 100 %. In the follow-up phase
Bill maintained his on-task behavior at a mean of 91.1 %.
The third panel in the graph in Fig. 1shows Paul’s
performance. The mean percentage of on-task behavior for
Paul during baseline was 28.2 %. The trend for his baseline
data revealed a significant deceleration, which warranted
the need for intervention. During training his mean per-
centage of on-task behavior was 95.3 % with an acceler-
ating trend, self-monitoring 98.6 %, self-monitoring plus
graphing 100 %, and fading 96.3 %. During follow-up,
Paul’s performance was more variable than during any of
the intervention phases and some of the problem behaviors
he demonstrated during baseline began to reemerge. As a
result, he was given a booster session in which he wore the
watch set to the 5-min interval and assessed his perfor-
mance. Immediately after asking Paul to get his watch and
checklist, the problem behaviors diminished and he com-
pleted all of his work with 100 % of intervals on-task. In
the final follow-up probe he was able to maintain his on-
task behavior at 100 %. Paul’s mean percentage on-task
during this phase was 81.7 %. Paul’s data in the follow up
phase demonstrated a decelerating trend unlike the other
intervention phases.
The fourth panel in the graph in Fig. 1represents Tom’s
performance. Tom’s baseline data were variable ranging
from 7 to 100 % throughout the 32 baseline sessions;
however, overall the trend within this phase was deceler-
ating. His mean percentage of on-task behavior during
baseline was 41.3 %. His mean percentage of on-task
behavior in the training phase was 96 %, self-monitoring
98.6 %, self-monitoring plus graphing 100 %, and fading
100 %. Tom’s data showed more variability in the follow-
up phase than any of his intervention phases with a mean of
88.1 %, a decelerating trend that was not evident since the
baseline phase. While this percentage of on-task behavior
can be seen as acceptable during follow-up, he had four
sessions at 83 %, which had not occurred since the
beginning of the training phase.
In addition to measuring on-task behavior, participants’
recording accuracy was measured during at least two ses-
sions in each intervention phase for the four participants.
The results revealed that all participants remained accurate
in their self-recording behavior with a mean of 97.3 %
accuracy.
Finally, percentage of non-overlapping data (PND) was
calculated for each participant in order to assess the effect
of the independent variable on the dependent variable (Gast
2010). For Adam and Bill PND equaled 100 %, and for
Paul it was 92 %. In contrast, due to variability, three of
Tom’s baseline data points overlapped once the interven-
tion was introduced.
Discussion
This study was conducted to answer three research ques-
tions. The first question was whether WatchMinder was an
effective prompting device for increasing on-task behavior
of students with ASD. Based on the functional relationship
that was noted through replicated results across all four
participants, it was evident that the WatchMinder was an
effective prompting tool that contributed to their increased
on-task behavior. All participants were able to increase
their work productivity and independence while working at
their seats.
The second question sought to determine the degree to
which self-graphing and immediately analyzing progress
J Autism Dev Disord
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had an effect on the participants’ ability to increase on-task
behavior. Compared to the self-monitoring phase, the
addition of the graphing component contributed to a slight
increase in on-task behavior for all participants except for
Adam whose mean percentage of on-task behavior dropped
slightly from 93.8 % to 92.2 %. It should be mentioned
that since the amount of on-task behavior was already
above 90 % for each participant, there was not much room
for improvement when the graphing component was added.
In addition, all participants verbally expressed that they
liked graphing their results and were enthusiastic about
discussing how well they were doing. Based on this
information, the addition of the self-graphing component
could have contributed to the increase in on-task behavior.
However, it also could have been attributed to the partic-
ipants becoming more comfortable and proficient with self-
monitoring. Further research on the addition of this com-
ponent is needed to determine the full effect of adding the
graphing component into the intervention package.
The third research question asked whether participants
would be able to maintain self-monitoring skills when
WatchMinder was removed. According to the data, all
participants were able to maintain their self-monitoring
skills and their on-task behavior at a higher level than
baseline. However, there was a decrease in the mean per-
centage of on-task behavior during follow-up when com-
pared to previous intervention phases. Paul and Tom’s
follow-up data showed more variability during follow-up
than any of the intervention phases. In addition, when the
intervention was removed, Paul’s problem behaviors began
to redevelop as they did during baseline. The follow-up
sessions revealed the same type of variability and slight
decrease in on-task behavior for each participant.
Based on the information yielded from the follow-up
data, a few assumptions can be made. First, the self-mon-
itoring program may have been faded too quickly. A more
systematic fading procedure may have been effective in
helping participants maintain their self-monitoring skills.
Rather than abruptly stopping use of the WatchMinder,
requiring participants to wear it 3 days per week in the
fading phase may have promoted increased maintenance of
the skills once the WatchMinder was removed. Second,
some individuals may require brief booster retraining ses-
sions to help them maintain their skills. Therefore, a
booster session can be used in which the individual would
wear the WatchMinder for one session. The effectiveness
of a booster session was demonstrated with Paul during the
follow-up phase. Two booster sessions were also used with
Adam after his cycles with illness when data became var-
iable. After returning to the 2-min interval for two sessions,
he was able to move back to using the 5-min interval
successfully. While it is the goal for individuals to be able
to maintain self-monitoring skills without prompting, this
type of device would also be appropriate for long-term use
for those with a greater degree of inattention (Milley and
Machalicek 2012).
Self-monitoring is a lifelong skill that can be used in
many facets of one’s daily lives. There were several ben-
efits to using this self-monitoring package in the classroom.
First, it reduced the number of verbal prompts the teacher
needed to give to her students. This allowed her more time
to focus on the students she was teaching in a small group
and it reduced problem behaviors that occurred from giving
numerous prompts to the same students. McDougall et al.
(2012) also indicated freeing up teacher time for more
productive tasks as a benefit of teaching self-monitoring
skills in the classroom.
In many classrooms teachers take the full responsibility
for grading and reporting progress. However, when stu-
dents become active participants in their educational pro-
grams, they can be more accountable for their performance
and therefore need to self-manage themselves effectively.
This can foster an increase in motivation, responsibility,
self-reliance, and independence, which are skills all
teachers should help students develop.
Another benefit to this type of self-monitoring program
is the ease with which classroom teachers are able to
implement it. Since students are responsible for monitoring
themselves, it takes the pressure off of the teacher to per-
sistently provide prompts. Amato-Zech et al. (2006) cited
minimal teacher demands or curricular modifications to be
a benefit of self-monitoring programs. With fewer demands
on the teacher and more responsibility on the students, the
WatchMinder intervention was ideal for this classroom
environment. It may be more manageable for general
education environments when there are a large number of
students to monitor. However, it is ideal for a special
education classroom because of varying student schedules
and activities.
Tactile self-monitoring programs such as the one used in
this study can be considered socially valid interventions
because they are unobtrusive and contribute to a drastic
increase in acceptable classroom behavior. Because the
WatchMinder vibrates rather than beeps, most other stu-
dents in the classroom are unaware of the watch going off.
It also allows multiple students to wear watches based on
their individual needs. One student can wear a watch that
vibrates every 2 min, and another can wear one set to
vibrate at every 5 or 10 min. Also, since the WatchMinder
looks like a regular sports watch, it does not make the
person wearing it stand out amongst others. In addition to
being an unobtrusive prompting device, the data from this
study, as well as others that investigated tactile prompting
devices, showed a drastic increase in socially acceptable
classroom and community behaviors (Green et al. 2011;
Legge et al. 2010; Van Hulle and Hux 2006). In the
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classroom setting, when on-task behavior of a few students
increases, the dynamic of the classroom can change and
more learning can occur for everyone. Therefore, lessons
and activities may flow better because more content can be
covered.
Finally, it is possible that after developing self-moni-
toring skills, some students may be able to increase the
amount of time they spend in general education classes.
For example, once Tom learned to be aware of the specific
behaviors he demonstrated were off-task, he was able to
complete more work independently, and by the end of the
school year Tom entered a general education classroom full
time. This may not be solely a result of the WatchMinder
intervention; however, it played a significant role that
helped him gain the few skills he needed to help him keep
up in the general education setting. It should also be noted
that intervening in spite of Tom’s unstable baseline did
provide clinical benefit since he was able to increase his
time on-task on a more consistent basis than during
baseline.
Limitations
There were several limitations to this study. One limitation
was the fact that two of the four participants had been
previously exposed to the WatchMinder during the previ-
ous school year. In the previous school year, Adam used
the WatchMinder to target participating in a small group
lesson during reading instruction. While the watch cued
him to assess his behavior similar to how it was used in this
study, it was not used systematically or on a daily basis.
Tom used the WatchMinder to target completing tasks
within a given time period using the reminder mode. The
watch vibrated once at the end of his work session and if he
was finished with his task he earned a star on his behavior
chart. This procedure is different from the one used in the
current study because the watch did not provide prompts
cueing him to monitor his behavior during his work ses-
sion. Since the watch only vibrated once at the end of his
work session, Tom’s unstable baseline data in this study
should not be attributed to previous exposure since the
method in which he used the watch was very different and
did not target on-task behavior. In addition, aside from
Tom’s unstable baseline, the results of the participants who
had pervious exposure to WatchMinder were no different
from the other two participants. While intervening for Tom
in spite of his unstable baseline is a limitation of this study
because it broke the research protocol for this intervention,
it was done for the benefit of the student rather than for
scientific purposes.
Other limitations were winter break and student illness.
Winter break did not appear to have an effect on the data
for Adam, Bill, and Paul who had already been introduced
to the intervention program. However, Tom’s baseline data
was impacted for a few days when he returned from break.
During the study period, all participants missed at least
2 days of school due to illness. However, Adam missed a
total of 24 days of school due to a virus. After he began the
cycle of illness, his data became more variable.
There were also some limitations related to the Watch-
Minder itself. First, the WatchMinder has precise charging
procedures. If it was not charged properly and lost battery
power, it took several hours to recharge and reset. The
battery indicator did not always show when it needed to be
charged. Therefore, it was important to keep track of the
last time the watches were charged and to make sure they
were in fact charging once plugged in. Second, there is
only a fixed interval option on the WatchMinder. However,
a variable interval may be more effective so students
cannot anticipate when the watch will go off. Legge et al.
(2010), Holifield et al. (2010), and Amato-Zech et al.
(2006) also called for the need for a variable interval
schedule so behaviors would be more resistant to
extinction.
An additional limitation of this study was that treatment
fidelity data were only taken for two of the four partici-
pants. Several other responsibilities for the classroom
paraprofessional, including transporting students to other
classes and assisting in fine arts classes, contributed to
limited availability to collect treatment fidelity data for
Adam and Bill. While she was in the room for a portion of
their session, she would have been unable to consistently
collect data on all steps of the training procedure. The
classroom schedule was altered during the training phase
so the paraprofessional was available to collect the
remaining treatment fidelity data as well as IOA data later
in the study.
Implications for Future Research
The use of tactile cued self-monitoring remains the most
underutilized form of self-monitoring interventions
(McDougall et al. 2012). However, the research on
WatchMinder and other tactile prompting devices such as
MotivAider are promising. The results demonstrated that
these self-monitoring programs are effective and can assist
students to become more aware of their behaviors. How-
ever, there are many aspects of these programs that will
require more research.
Future research should examine the effects of different
fading procedures on participants’ ability to maintain self-
monitoring skills for an extended period of time. Various
fading procedures including extending the time interval
and extending the number of days the watch is worn per
week should be compared. It will be important to discern
how long students are able to maintain these skills to
J Autism Dev Disord
123

predict whether booster sessions are likely to be needed
throughout a person’s lifetime to help him/her maintain this
essential skill, or whether continued use will be necessary.
Since there are many components that can make up a
self-monitoring intervention package, the effect of specific
components in conjunction with WatchMinder should be
examined. For example, the effect of reinforcement being a
part of the intervention package should be assessed. It is
possible that there were added motivating operations in
place when the reinforcer component was added and it may
have affected the magnitude of the behavior change.
Another component that should be researched is the effect
of the self-graphing on the percentage of on-task behavior.
In this study, self-graphing contributed to an increase in on-
task behavior, but it was not clear whether self-graphing
was the only factor that caused the increase since partici-
pants were also becoming more comfortable and proficient
with the self-monitoring procedures.
The results of this study should also be extended to other
behaviors and settings. Future research should examine the
effect of students with ASD using WatchMinder in the
general education setting. Since many students with ASD
struggle to participate in general education classes due to
difficulty focusing and keeping pace with the group, this
may be an effective intervention for teaching the specific
behaviors required for monitoring task engagement in a
large group setting. Finally, future research should address
generalization of self-monitoring skills to other subject
areas, from individual to small group settings, from small
group instruction to whole group instruction, and across
target behaviors.
Acknowledgments The authors thank Elisa Cruz-Torres for
assisting us with graphing.
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