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Prompting technologies: A comparison of time-based and
context-aware transition-based prompting
Kayela Robertsona, Cody Rosascoa, Kyle Feuzb, Maureen Schmitter-Edgecombea,*, and
Diane Cookc
aDepartment of Psychology, Washington State University, Pullman, WA, USA
bDepartment of Computer Science, Weber State University, Ogden, UT, USA
cSchool of Electrical Engineering and Computer Science, Washington State University, Pullman,
WA, USA
Abstract
BACKGROUND—While advancements in technology have encouraged the development of
novel prompting systems to support cognitive interventions, little research has evaluated the best
time to deliver prompts, which may impact the effectiveness of these interventions.
OBJECTIVE—This study examined whether transition-based context prompting (prompting an
individual during task transitions) is more effective than traditional fixed time-based prompting.
METHODS—Participants were 42 healthy adults who completed 12 different everyday activities,
each lasting 1–7 minutes, in an experimental smart home testbed and received prompts to record
the completed activities from an electronic memory notebook. Half of the participants were
delivered prompts during activity transitions, while the other half received prompts every 5
minutes. Participants also completed Likert-scale ratings regarding their perceptions of the
prompting system.
RESULTS—Results revealed that participants in the transition-based context prompting
condition responded to the first prompt more frequently and rated the system as more convenient,
natural, and appropriate compared to participants in the time-based condition.
CONCLUSIONS—Our findings suggest that prompting during activity transitions produces
higher adherence to the first prompt and more positive perceptions of the prompting system. This
is an important finding given the benefits of prompting technology and the possibility of
improving cognitive interventions by using context-aware transition prompting.
Keywords
Prompting technology; cognitive intervention; assistive technology; cognitive aids
*Corresponding author: Maureen Schmitter-Edgecombe, Department of Psychology, Washington State University, Pullman, WA
99164-4820, USA. Tel.: +1 509 335 0170; Fax: +1 509 335 5043; schmitter-e@wsu.edu.
Conflict of interest
The authors report no conflicts of interest.
HHS Public Access
Author manuscript
Technol Health Care. Author manuscript; available in PMC 2016 March 22.
Published in final edited form as:
Technol Health Care. 2015 ; 23(6): 745–756. doi:10.3233/THC-151033.
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1. Introduction
Advances in technology have fueled new opportunities for health care assistance,
particularly in the field of neurorehabilitation where individuals often need reminders or
prompts to assist with activity initiation and completion [1]. Research suggests that
prompting technologies, that is, any form of verbal or non-verbal intervention delivered to
the user [2], can be beneficial for individuals with cognitive impairments [3–5]. Prompting
technologies can be as simple as reminders, notifications, and alerts, but also can be more
complex, such as machine learning algorithms that monitor an individual’s behavior and
prompt during specific tasks [3,6,7]. To further development of automated prompting
technologies, we evaluated the importance of prompt timing by investigating whether
transition-based prompting is more effective than time-based prompting.
A fixed time-based prompting system delivers prompts based solely on a pre-specified and
inflexible time, similar to how a kitchen timer or alarm clock works. Time-based prompting
has been shown to be effective in promoting task engagement, task completion, and focus
[8,9]. For example, individuals with intellectual disabilities became more engaged in tasks
after being taught to look at instructional cards when prompted at predetermined times [10].
In addition, several studies have shown an increase in prospective memory performance
after subjects with brain injuries used a paging device that delivered time-based prompts for
task completion [11,12].
One limitation of time-based prompting is that it may be delivered when the user is engaged
in another important task. Also, time-based prompting often requires the user to do extra
work (i.e., programming a schedule). For example, Ferguson, Myles, and Hagiwara [13]
found that while using a handheld computer helped a child with autism complete IADL’s
more independently, the device was very hard for the parents and teachers to use and also
required a significant amount of user input before it was effective. A user may also become
annoyed after hearing a prompt to do a task that has already been completed and problems
may arise due to the dynamic nature of some daily activities. An untimely prompt can also
increase cognitive load and reduce user attention to the intervention [14]. According to
Seelye and colleagues [15], incorporating activity awareness into time-based prompting
would address these and other similar problems. Time-based prompting with activity
recognition effectively describes activity-aware prompting, a class of context-aware
services.
Context-aware prompting systems use the environment and the status of a person (e.g., an
individual’s location) to recognize effective prompting contexts. In location-based context-
aware prompting, prompts are provided based on the location of the user, often utilizing
GPS technology (e.g., prompt when near a grocery store [16], prompt based on a to-do list
[17]). For example, Frazer and colleagues [18] devised a system to improve GPS navigation
in electric vehicles that decreased glancing at the GPS interface while driving by prompting
the driver based on relevant location information. Recently, Ramanathan and colleagues [19]
began exploratory research on the development of a smartphone-based system, and other
studies have used the Wizard-of-Oz techniques or wireless sensor networks to develop a
prompting system that improves indoor wayfinding and/or task initiation for individuals
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with cognitive impairments [20–22]. Similar to time-based prompting, the main limitation of
location-based prompting is that the most effective time to prompt may not be dependent on
the location of the user, but on the activities the user is or has been engaged in.
Context-aware systems generally use a fixed set of parameters such as time and location to
identify a current situation. Not only is the parameter set typically fairly small, but the
parameters are considered separately. In contrast, activity-based context-aware prompts take
into account a richer state description, expressed as the user’s past and current
automatically-recognized activity. For example, Lancioni and colleagues [23] found
improvements in reading ability when a computer sent a tactile prompt upon registering
inactivity while a child with ADHD was told to read information off of a screen. Another
study used a gestural recognition system to prompt individuals to complete various
vocational activities, which also proved useful in increasing task success rates [24]. Several
studies found that activity-based context-aware prompting helped people remember to take
their medications and increased treatment adherence compared to time-based prompting
[25,26]. These systems improved upon the prompting systems described previously by
prompting only when the machine-learning algorithm predicted that the user might not take
the medication, and using multiple methods of determining context, which included motion
sensors and a wearable location sensor. Despite an improvement over classic time-based
prompting methods, context-aware methods of prompting still have limitations. Most studies
done using context-based prompting methods still require user intervention to make the
prompting work, such as requiring user feedback, or requiring a user to input a schedule
[15].
This study aims to provide prompts at times when it would be most opportune for the user to
receive them, and therefore respond to them. When delivery of a prompt is based on time or
location, the user may or may not be currently engaged in an activity where prompting
would be inappropriate or inconvenient. To address this concern, prompting during
transition periods, a period of time when the user is not engaged in an activity and may be
transitioning between activities, has been suggested as an effective prompting time
[14,27,28]. Instances of information overload can occur when prompts are delivered during
an activity [27], and individuals typically perform better on tasks if they are only doing one
activity at a time [29,30]. Additionally, interruptions during a task can increase the risk of
making errors on that task [31–33]. Therefore, if prompting a user to perform an activity
occurs while the user is engaged in another activity, time, number of errors and task
completion of each activity may be affected.
This study assessed whether activity transition-based prompting improves use and
perceptions of prompts to use a digital memory notebook compared to traditional time-based
prompting. Participants completed twelve tasks of everyday living (such as cooking,
cleaning, and watching television) in a smart home. While completing the tasks, participants
received prompts delivered by the experimenter to record information into a digital memory
notebook either every five minutes (time-based condition) or during activity transition
periods (transition-based condition). At the end of the experiment, participants were asked to
critique the timing of the prompts in order to evaluate the effectiveness of the prompt
timing. Based on dual-task management theory, which suggests that prompting individuals
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during a transition period (rather than during a task) will lessen cognitive load [27], we
hypothesized that transition-based prompting would result in a greater number of responses
to the first prompt and greater user satisfaction compared to time-based prompts.
2. Methods
2.1. Participants
Participants were 42 undergraduate students recruited from a local university. They were
randomly assigned to either the transition-based prompting condition or the time-based
prompting condition. The majority of students received credit in an introductory psychology
course in return for their time.
2.2. Procedure
Participants completed an hour-long testing session in a campus smart apartment.
Participants were asked to complete twelve everyday tasks in a predetermined randomized
order, including putting together a puzzle, dusting the living room, gathering cooking
ingredients, and reading a magazine (see Table 1 for complete list). Each task took between
one and seven minutes to complete. To ensure smooth activity transitions, the experimenter
read the directions for the next task while the participant was still completing the previous
task via an intercom system. While completing the experiment, participants received
experimenter initiated audio prompts via an Android tablet. The prompts asked whether they
would like to record the completed activities into a daily log on the tablet. If they chose to
do so, participants entered the completed activities by either typing on the keyboard or using
the voice-to-text system.
Participants were randomly assigned to either the time-based or transition-based prompting
condition. For the time-based condition, prompts were delivered every five minutes. For the
transition-based condition, prompts were delivered during each of the transition periods
between activities and followed strict transition start and stop guidelines (see Table 1).
Activities began once participants retrieved the first item required to perform the task at
hand, and ended once the last item used to perform the current task was returned to its
original position. Transition start times were marked by the participant ending their current
task (e.g., the participant is done dusting and is putting away the duster). Transition stop
times were defined by the participant beginning a new task (e.g., the participant picks up the
magazine to start reading). Of note, there was a small variance in the number of transition-
based prompts delivered between participants in the transition-based condition due to lack of
clear transition points between certain tasks depending on the randomized order of the tasks.
If the participant chose to disregard the original prompt, another prompt was issued one
minute later. Participants received the following information about the prompts, “if it’s a
convenient time for you, record what activities you have completed when the memory
notebook prompts you. If the memory notebook prompts you at a bad time, that’s okay, it
will re-prompt you in a few moments and you can respond then”.
While participants were completing the tasks, the experimenter observed from a separate
room, watching participant performances through live feed video and communicating
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through an intercom system. A number of motion sensors installed in the smart apartment
were used to track the participant’s movements. The experimenter used the Real-time
Annotation Tool (RAT) [34] to record what the participant was doing, when activity
transitions occurred, and when prompts were delivered. The RAT is a program developed to
replace paper-pencil coding by using a computerized annotation system that allowed
experimenters to accurately time-stamp participant’s actions sequentially. Smart home
sensor data and experimenter notes from the RAT were automatically stored in an SQL
(Structured Query Language) database for later analysis.
2.3. Outcome measures
To investigate the effectiveness of time-based versus transition-based prompting we
included multiple outcome measures. First, we recorded the number of times each
participant used the memory notebook and how many first prompts were administered to
each participant (which excluded re-prompts). Additionally, we coded whether participants
responded to the first prompt, the time at which prompts were delivered, and the time at
which participants responded to the prompts by writing in the memory notebook (i.e., during
an activity, which meant interrupting the activity, or during a transition period). We also
recorded how long it took participants to complete all twelve tasks. At the end of the
experiment, we had participants complete several questions to evaluate their perceptions of
the prompting system. Participants were asked whether the prompts were delivered at
convenient times, whether the timing seemed natural, and whether the timing seemed
appropriate. Each question was rated on a seven-point scale: strongly agree, agree, slightly
agree, neutral, slightly disagree, disagree and strongly disagree, with higher ratings
indicating a better system. Six participants (three in each condition) did not receive the
questions due to experimenter oversight.
3. Results
3.1. Analyses
To test our hypotheses, a series of independent samples t-tests were performed on the
outcome measures. For the main outcome measures, we also calculated Cohen’s d values to
examine effect sizes. General guidelines for interpreting Cohen’s d effect sizes are as
follows: 0.2 is a small effect size, 0.5 is a medium effect size, and 0.8 is a large effect size
[35]. We began by comparing participants in each group to examine whether differences
between age, education, or gender existed. To ensure that the experimental manipulation
was effective, we then examined whether the transition-based condition received more
prompts during transition periods compared to the time-based condition. We then conducted
t-tests to determine whether there were differences between conditions in the number of
prompts that yielded a response to the first prompt. T-tests also evaluated whether the
conditions differed on several other variables including (a) the number of times the
participant’s response to the prompt interrupted the ongoing task, (b) the number of times
the participant used the notebook during a transition period (a pre-determined period of time
indicated in Table 1), and (c) the time in took participants to complete all twelve tasks. In
addition, examination of differences between conditions in perception of the prompting
system was evaluated using t-tests. Finally, correlation analyses were conducted to examine
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associations between the prompting variables and participants’ perceptions of the prompting
system. Given the number of correlations conducted, the significance level was set at p <
0.01.
3.2. Comparing participant variables
First, we compared age, education, and gender between participants in each condition. Of
note, not all participants completed the questionnaire form; therefore, we do not have data
on age, education, and gender for all of our participants. Participants in the transition-based
condition had an average of 12.42 years of education and those in the time-based condition
had 12.47 years of education, which was not statistically significant, t(32) = −1.16, p = 0.27.
Mean age of participants in the transition-based (19.88 years) and time-based (21.59 years)
conditions also did not differ, t(32) = −0.24, p = 0.81. Furthermore, there were no
differences between gender in the two conditions, X2(1, N = 32) = 0.00, p = 1.00 (6 males,
11 females, and 4 unknown in each condition).
Due to variance in participant’s speed of completing each of the tasks, participants did not
receive an equal number of prompts. Also, participants in the transition-based condition
were not always administered the same amount of prompts due to lack of clear transition
points between certain tasks depending on the randomized order of the tasks. However, there
was no significant difference between the number of first prompts administered to
participants in the transition-based condition (M = 10.00, SD = 1.95) and the time-based
condition (M = 9.52, SD = 1.40), t(40) < 1; therefore, we were able to use the raw scores in
the data analyses. The results remained the same when the data were examined as proportion
scores by adjusting for the number of prompts administered.
3.3. Comparisons of prompting responses
Participants in the transition-based condition received significantly more prompts during
transition periods (M = 9.14, SD = 2.31) than participants in the time-based condition (M =
0.70, SD = 0.80), t(40) = 15.12, p < 0.001, d = 4.89, indicating that the experimental
manipulation was effective. Overall, there were no differences between the transition-based
condition (M = 7.95, SD = 3.38) and the time-based condition (M = 8.05, SD = 2.91) in the
number of times that the memory notebook was used, t(40) < 1. As seen in Table 2,
participants in the transition-based condition responded to the first prompt an average of
7.05 times, while those in the time-based condition responded an average of 4.24 times,
which represented a significant difference between conditions, t(40) = 3.19, p < 0.005, d =
0.98. These findings support our main hypothesis that participants in the transition-based
condition would be more likely to respond to the first prompt than those in the time-based
condition.
Participants in the transition-based condition interrupted an ongoing tasks 0.67 times on
average to write in the memory notebook, while participants in the time-based condition
interrupted tasks 2.76 times on average, t(40) = −4.85, p < 0.001, d = 1.49 (see Table 2).
Participants in the transition-based condition used the memory notebook an average of 7.29
times during transition periods while participants in the time-based condition used the
memory notebook during transition periods an average of 5.29 times, which trended towards
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statistical significance, t(40) = 2.00, p = 0.05, d = 0.62 (Table 2). Of note, the large number
of responses at the transition periods by participants in the time-based condition suggests
that even though prompts were not administered during transition periods, participants
preferred to wait until a transition period occurred to respond to the prompt by writing in the
note-book. Finally, participants in the transition-based condition completed all of the tasks
an average of 5.54 minutes quicker than participants in the time-based condition, which was
statistically significant, t(40) = −2.34, p = 0.02, d = 0.72.
3.4. Comparison of prompting system ratings
We also expected that individuals in the transition-based condition would have more
positive perceptions of the prompting system. As seen in Table 3, participants in the
transition-based condition rated prompt timing as more convenient, t(32) = 4.04, p<0.01, d =
1.39, with a mean rating of 6 (“agree” that the prompt was delivered at convenient times)
compared to a mean rating of 4 (“neutral” that the prompt was delivered at convenient
times) for the time-based condition. Participants in the transition-based condition also rated
prompt timing as more natural t(32) = 3.95, p < 0.01, d = 1.35, with a mean rating of 6
(“agree” that the prompt timing was natural) compared to a mean rating of 5 (“slightly
agree” that the prompt timing was natural) for participants in the time-based condition.
Finally, participants in the transition-based condition rated the prompt timing as more
appropriate, t(32) = 4.09, p < 0.01, d = 2.57, with a mean rating of 6 (“agree” that the
prompt timing seemed appropriate) compared to a mean rating of 5 (“slightly agree” that the
prompt timing seemed appropriate) for the time-based condition.
We then examined whether individuals’ perceptions of the prompting system were
associated with any of the prompting variables. As seen in Table 4, individuals rated the
system as more convenient when less re-prompts had to be delivered, r(34) = −0.46, p <
0.01, when they were able to respond to the first prompt more frequently, r(34) = 0.54, p <
0.01, and when they responded more frequently to prompts during transition periods, r(34) =
0.43, p < 0.01. Participants also rated the prompt timing as being more natural when less re-
prompts had to be delivered, r(34) = −0.45, p < 0.01.
4. Discussion
Advancements in technology have encouraged the development of novel assistive
technologies, which have enhanced neurorehabilitation techniques and broadened our ability
to aid individuals with cognitive deficits [3–5]. However, because technologies evolve so
quickly, it is often difficult to understand the various components of a particular technology
and what makes it more or less efficacious. For example, although numerous prompting
technologies have been employed, factors that impact the effectiveness of prompts (e.g.,
timing of prompts) are not yet fully understood. Thus, the purpose of this study was to
examine whether transition-based prompting (i.e., prompting an individual during activity
transition) is more effective than traditional time-based prompting. Participants completed
12 different IADLs in a smart home and received either time-based prompts or transition-
based prompts to record information in a digital memory notebook.
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Consistent with our hypothesis, participants were more likely to respond to the first prompt
when in the transition-based condition, thereby requiring less re-prompting. Participants in
the transition-based condition rated the prompting system as being more convenient, natural,
and appropriate. This demonstrates that receiving transition-based prompting not only
results in higher user responses to initial prompts, it also produces more positive perceptions
of the system, which is imperative for any novel device. More positive perceptions of the
system were also associated with less re-prompting and ability to respond to prompts the
first time and during a transition period. Participants were also able to complete the tasks
more quickly when transition-based prompts were delivered, which is likely a result of
having less reprompts and less task interruptions. These findings suggest that prompting
systems should try to avoid the need to re-prompt an individual, emphasizing the advantage
of transition-based prompting. Furthermore, our analyses revealed that individuals were less
likely to interrupt a task to record information in the memory notebook when prompted
during transition periods. The dual-task management theory proposes that individuals will
be more likely to respond to prompts that do not interrupt activities because the cognitive
load is lessened [27]. This may also account for the finding that participants in the time-
based condition often waited for a transition period to occur to respond to a prompt, even
though the prompt was administered during a task. Thus, our study suggests that a
prompting system that utilizes knowledge of activity transitions will be more effective than
traditional time-based reminder systems.
This is the first study to evaluate the effectiveness of prompting during activity transitions;
however, the literature has investigated other types of context-aware prompting, such as
location and activity context-based prompting. Location based prompting systems often
utilize GPS systems to alert individual’s when they are near a specific location, which can
be helpful; however, they do not correct for potential problems of information overload.
Activity context-based prompting is similar to transition-based prompting because it
attempts to adjust for selected activity contexts such as when a person is out of the home or
is sleeping. Similar to our results, many of these activity based prompting studies have
concluded that context-based prompting is more effective than traditional methods [25,26].
As Lundell et al. [25] described, “a simple time-of-day rule to trigger an alarm, are not very
effective because the reminder is generated whether or not it is an opportune time or place”
(p. 1). In fact, we also observed that participants who were interrupted during a task were
more likely to respond during transition periods or to interrupt inactive tasks (e.g., reading a
magazine) compared to active tasks (e.g., making oatmeal). This highlights the importance
of integrated prompting systems that take advantage of “opportune” moments. This is
particularly meaningful for real-world applications because many activities will take more
time than those assessed in this study, which would likely result in more delayed (and
potentially forgotten) opportunities to use the memory notebook. Although our results are
similar to other context-based studies, our system defines this “opportune” time as a period
of activity transition, rather than inactivity or engagement in a more docile task or location.
Therefore, future studies would benefit from comparing and/or integrating these different
types of context-aware prompting systems in order to develop the most effective system.
Future studies will also be needed to demonstrate that these findings will generalize to
cognitively impaired populations.
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To experimentally test the effects of time-based and transition-based prompting in the
laboratory, we had to set up a design that resulted in the administration of nearly 10 prompts
within a one-hour time period. This is different from the frequency of prompting that would
be expected in the real world environment and may limit generalization. In addition, some of
the activities in this study took only a brief amount of time to complete such that the time
between activity completion and the time-based prompt may have been significantly
delayed. This too may differ from time-based prompting delivered in a real-world
environment.
Although we believe that a transition-based activity-aware prompting system can be useful
in many ways, several hurdles must be overcome before such systems are a reality. In the
current study, the experimenter issued the prompts, which is not practical outside of an
experimental testbed. For this reason, we developed an activity recognition algorithm that
has the ability to detect transitions and deliver prompts automatically during activity
transitions, thus eliminating the need for human intervention [36]. In a separate study, we
evaluated detection of transition periods in scripted and unscripted environments and found
that the recognition algorithm was able to detect transition periods greater than 80% of the
time, with a false positive rate of less than 15% [37]. However, our completed prompting
system would require users to have infrared motion sensors installed in their homes. The
home-based sensor system would require very little infrastructure and set-up, but it can seem
obtrusive. Of note, most studies suggest that this is a small cost to pay if it allows
individuals to remain independent in their homes [15]. Unfortunately, this will limit the
system to in-house use only, but other back-up prompting systems can be employed if the
user is out of their house when a prompt is needed. It may also be possible to integrate this
prompting system into a smartphone, but currently sensors in existing smartphones would
not be able to detect transition periods as accurately as a home-based sensor system. It will
be important to test this system as a whole, particularly with individuals with cognitive
impairment because they are the targeted user for most cognitive interventions. We also
hope to expand the use of prompts incorporated into the digital memory notebook. For
example, instead of prompting only to use the notebook, prompts to complete particular
activities on a to-do-list or prompts to take certain medications listed in the medications
section of the notebook can be added. This type of intervention system could be a useful
compensatory tool for many individuals with cognitive impairments resulting from
neurodegenerative diseases, such as dementia, or acquired neurological conditions, such as a
traumatic brain injury. Although the prompting system has not yet been tested by those with
cognitive impairment, we would expect the results to be the same, if not amplified. We base
this on the theory that transition-based prompting is more effective because it reduces
cognitive load, which will especially be important when working with individuals with
already compromised cognition.
Overall, an effective prompting system can improve the efficacy of cognitive interventions,
minimize the necessity for human assistance, and allow users to feel more independent.
Research has shown that prompting technologies can enhance medication adherence,
improve the use of external compensatory strategies, reduce caregiver burden, and increase
functional independence in those with cognitive impairment [4,5,38]. Our research provides
empirical evidence that prompting during activity transitions produces higher user response
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rates to the initial prompt, which is important so that the prompt is not forgotten, and more
positive perceptions of the system, which is crucial for any prompting system. Furthermore,
our proposed prompting system requires minimal user-intervention once set-up is complete
and is ideal for people experiencing memory problems that may need external cues to
successfully complete everyday activities. For people who are experiencing cognitive
difficulties due to traumatic brain injuries, stroke, and neurodegenerative processes, it is
imperative to develop technologies that will allow them to function more independently in
their own homes; thereby reducing cost of health care and burden of caregivers [39].
Therefore, it is essential that prompting systems continue to be investigated and employed in
the most effective manner possible.
Acknowledgments
This work was supported by a grant from National Science Foundation under grant No. DGE-0900781 and the
National Institute of Biomedical Imaging and Bioengineering under grant No. R01 EB009675.
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Table 1
Tasks performed by participants and their respective transition definitions
Activity Transition start (e.g.,
ending of task) Transition end (e.g.,
beginning of task) Average time on
task (seconds)
Dust the bottom level of the apartment Puts the duster away Begins to retrieve the duster 143.68 ± 81.18
Copy a recipe in the kitchen Puts the last task item back Begins to retrieve the first task
item 248.89 ± 125.96
Pick out an interview outfit and then put it
back Puts the last item of clothing back Begins to retrieve the first item of
clothing 76.97 ± 49.80
Read a particular page in a magazine Puts the magazine back Begins to retrieve the magazine 210.80 ± 128.01
Gather ingredients to make spaghetti and then
put them back Puts the last task item back Begins to retrieve the first task
item 145.70 ± 50.39
Watch television in the living room Turns the television off Turns the television on 288.97 ± 83.49
Collect items on a list and put them in a
picnic basket Puts the last task item back Begins to retrieve the first task
item 246.00 ± 121.31
Work on a puzzle at the dining room table Puts the puzzle away Begins to retrieve the puzzle 325.04 ± 119.50
Sweep the kitchen Throwing away the dirt Begins to retrieve the broom 124.35 ± 44.71
Complete some math problems at the dining
room table Puts the pencil down and has all
questions completed Begins to retrieve the pencil and
begins problems 220.18 ± 98.02
Make a bowl of oatmeal Takes oatmeal out of microwave or
puts the last task item back
(whichever comes last)
Begins to retrieve first task item 206.15 ± 73.32
Play a handheld game at a chair by the door 5 minute time limit is up Begins to retrieve the game 293.25 ± 61.41
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Table 2
Comparison of prompting variables in the context-aware transition condition and time-based condition
Dependent variable
Transition-based condition
(n = 21)
M (SD)
Time-based condition
(n = 21)
M (SD) t-test p−value Cohen’s d
Number of memory notebook uses 7.95 (3.38) 8.05 (2.91) −0.10 p = 0.92 0.03
Number of first prompts given 10.00 (1.95) 9.52 (1.40) 0.91 p = 0.37 0.28
Response to first prompt 7.05 (3.34) 4.24 (2.28)** 3.19 p = 0.001 0.98
Task interruptions 0.67 (0.86) 2.76 (1.79)** −4.85 p = 0.001 1.49
Number of uses during transitions 7.29 (3.43) 5.29 (3.00) 2.00 p = 0.05 0.62
Time to complete all tasks 40.13 (7.19) 45.67 (8.11)*−2.34 p = 0.02 0.72
Note:
*p < 0.05,
**p < 0.01,
M = mean, SD = standard deviation, General guidelines for interpreting Cohen’s d effect sizes are as follows: 0.2 = small effect size, 0.5 = medium effect size, and 0.8 = large effect size [35].
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Table 3
Comparison of group ratings regarding the prompting system
Questions
Transition-based condition
(n = 17)
M (SD)
Time-based condition
(n = 17)
M (SD) t-test p–value Cohen’sd
System usability
The prompts were delivered at convenient times 6.00 (1.41) 4.06 (1.39)** 4.04 p = 0.001 1.39
The prompt timing seemed natural 6.35 (0.86) 4.71 (1.49)** 3.95 p = 0.001 1.35
The prompt timing seemed appropriate 6.24 (0.83) 4.65 (0.28)** 4.09 p = 0.001 2.57
Note:
p < 0.05,
**p < 0.01,
M = mean, SD = standard deviation, General guidelines for interpreting Cohen’s d effect sizes are as follows: 0.2 = small effect size, 0.5 = medium effect size, and 0.8 = large effect size [35].
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Table 4
Correlations between prompting variables and perceptions of system usability
Prompting variables
System usability
Prompting was
convenient Prompt timing was
natural Prompt timing was
appropriate
Number of memory notebook uses 0.25 −0.10 0.05
Number of re-prompts given −0.46** −0.45** −0.40
Response to first prompt 0.54** 0.26 0.30
Task interruptions −0.41 −0.36 −0.34
Number of uses during transitions 0.43** 0.10 0.23
Time to complete all tasks 0.05 0.02 0.01
Note:
*p < 0.05,
**p < 0.01.
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