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Keywords:

  • alertness observations;
  • alertness stimulation;
  • individuals with profound intellectual and multiple disabilities;
  • time-window sequential analysis

Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Material and methods
  5. Results
  6. Discussion
  7. Key messages
  8. Acknowledgement
  9. References

Background

While optimally activities are provided at those moments when the individual with profound intellectual and multiple disabilities (PIMD) is ‘focused on the environment’ or ‘alert’, detailed information about the impact that the design and timing of the activity has on alertness is lacking. Therefore, the aim of the present study is to shed light on the sequential relationship between different stimuli and alertness levels in individuals with PIMD.

Method

Video observations were conducted for 24 participants during one-on-one interactions with a direct support person in multisensory environments. Time-window sequential analyses were conducted for the 120 s following four different stimuli.

Results

For the different stimuli, different patterns in terms of alertness became apparent. Following visual stimuli, the alertness levels of the individuals with PIMD changed in waves of about 20 s from ‘active alert’ to ‘passive alert’. While auditory and tactile stimuli led to ‘alert’ reactions shortly after the stimulation, alertness levels decreased between seconds 20 and 120. Reactions to vestibular stimuli were only visible after 60 s; these were ‘active alert’ or ‘withdrawn’.

Conclusions

The results of the present study show that individuals with PIMD show their reactions to stimuli only slightly, so that ‘waves’ might reflect the optimal alertness pattern for learning and development. Consequently, it is especially important that direct support persons follow and stimulate these individual ‘waves’ in the activities they provide to their clients.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Material and methods
  5. Results
  6. Discussion
  7. Key messages
  8. Acknowledgement
  9. References

While previous studies have revealed that the optimal moments for activities are those when the individual with profound intellectual and multiple disabilities (PIMD) is ‘focused on the environment’ or ‘alert’ (Guess et al. 1999; Munde et al. 2009a), detecting those moments in individuals of the target group is difficult for researchers and direct support persons (DSPs). Individuals with PIMD experience a combination of profound intellectual, motor and sensory disabilities (Vlaskamp, 2005; Nakken & Vlaskamp 2007; Vlaskamp & Nakken 2008). As a result of damage to the central nervous system, individuals of the target group have a developmental age of 24 months or less. In addition, neuromotor dysfunctions result in limited use of the hands and arms, along with the need for a wheelchair. As a consequence, individuals with PIMD need pervasive support for all activities and for participation in daily life (World Health Organization 2001). Because of the severity and complexity of the disabilities, individuals of the target group only have a limited repertoire of body language signals for expressing themselves. In addition, alertness expressions are often subtle signals that can easily go unnoticed (Mudford et al. 1997; Guess et al. 1999). To complicate matters further, the same alertness expression can have a different meaning for different individuals, and even the same expression can have a different meaning for the same person in different situations (Hogg et al. 2001; Petry & Maes 2006).

Furthermore, DSPs such as care workers, teachers or therapists are left wondering how to promote those very alertness levels that are optimal for providing an activity. Previous studies have shown that stimulation in general can lead to higher alertness levels in individuals with PIMD (Belfiore et al. 1993; Ault et al. 1995; Guess et al. 1999; Lancioni et al. 1999; Arthur 2004). However, the same stimulation can lead to broadly varying reactions depending on the individual (Munde et al. 2009a,b). On top of that, the very way of presenting the activity can have an impact as well (Munde et al. 2009a). Integrating the use of assistive communication devices in order to choose an activity can, for example, result in other reactions in terms of alertness, as compared with a situation where the DSP chooses the activity unaided. Consequently, activities have to be adapted to the individual's preferences and needs.

In addition to the problem of the design of the stimulation, questions about its timing also remain unanswered. DSPs are not sure whether ‘being alert’ is a strict precondition for providing stimulation. Starting an activity when the individual with PIMD is withdrawn may also be a possibility in order to make that individual alert (Guess & Siegel-Causey 1995; Reese 1997). Moreover, alert periods of individuals with PIMD last only for a short time (Mudford et al. 1997). When an individual withdraws from the environment after ‘being alert’, DSPs may interpret this behaviour as a loss of interest on the part of the person with PIMD. This makes them inclined to stop the activity. However, quick and irregular changes between ‘being alert’ and ‘being withdrawn from the environment’ are common (Guess et al. 1995; Mudford et al. 1997), and the individual with PIMD might be alert again quite quickly after withdrawing attention from the activity. Determining the adequate duration of an activity is therefore another issue for DSPs.

While the initial studies on alertness in individuals with PIMD revealed that stimulation might have a greater impact on levels of alertness than the internal conditions of the individual, these studies only focused on the overall effect of stimulation (Guess et al. 1995; Guess et al. 1999; Arthur 2004). Other studies that relate alertness to different treatment activities have led to different results (e.g. Lindsay et al. 1997; Sandler & Voogt 2001; Perry 2003). When researchers investigated the relationship between alertness and different interaction strategies such as active choice making, microswitch-based stimulation and frequent prompts, they found that they promoted higher levels of alertness (Kennedy & Haring 1993; Lancioni et al. 2000; Lancioni et al. 2005). However, a detailed comparison of the timing of reactions to different stimuli in terms of alertness is lacking. Detailed analyses of different stimuli and alertness levels in individuals with PIMD could help in determining the optimal combination of ‘how’ and ‘when’ to provide stimulation. In addition, detailed information about the timing of changes in alertness levels could provide an answer to DSPs’ questions about when to start an activity and its duration.

The aim of the present study is to shed light on the sequential relationship between different stimuli and alertness levels in individuals with PIMD. For these analyses, we needed data from a situation where the stimulation could be controlled and alertness levels could be observed in detail. Because multisensory environments (MSEs) yield such possibilities, these were chosen as the experimental condition. In the 1980s, Hulsegge and Verheul were the first to introduce a form of MSE known as ‘Snoezelen’ in the Netherlands. To date, Snoezelen is the most frequently used form of MSE in Flanders (the Dutch-speaking part of Belgium) and the Netherlands (Vlaskamp & Nakken 2008). The term Snoezelen is a neologism which combines the two Dutch words snuffelen (to explore) and doezelen (to relax, to doze, to snooze), thus describing the characteristics of the activity. In this context, Snoezelen is understood as the opposite of being stressed. Snoezelen is carried out in specially equipped rooms in order to allow the individual with PIMD to experience the environment while excluding distractions. The DSP plays an important role during a Snoezelen session. Knowing the individual stimulus preferences and the optimal dosage of stimuli for his or her client, the DSP is able to present stimuli on an individual-interaction basis (Hulsegge & Verheul 1987). Although previous studies have led to a debate about the efficacy of MSEs (e.g. Kaplan et al. 2006; Williams et al. 2007; Tunson & Candler 2010), these environments still have the potential to serve as starting points for the process of determining and influencing alertness as described above. In the interactions of individuals with PIMD and their DSPs, different kinds of stimuli (e.g. auditory, visual, tactile, vestibular, olfactory or gustatory) are able to be presented separately or in combination, repeatedly or alternately. Distractions by other individuals or stimuli beyond the actual stimulating situation can be excluded. At the same time, accurate registration of the individuals’ behaviour and reactions is possible. This then leads to the central question of the present study: What is the sequential relationship between different alertness levels in individuals with PIMD and the different kinds of stimuli presented in an MSE?

Material and methods

  1. Top of page
  2. Abstract
  3. Introduction
  4. Material and methods
  5. Results
  6. Discussion
  7. Key messages
  8. Acknowledgement
  9. References

Participants

Nine daycare centres and schools for special education situated in Flanders (Belgium) and the Netherlands participated in this study. The facilities were randomly selected from the overall population of care centres and schools for individuals with PIMD that use MSE sessions for sensory stimulation. Between one and four clients within each facility participated in the study, for a total of 24 participants. The number of male and female participants was equal. The mean age was 15.66 years (SD = 12.02), ranging from 4 to 49. All participants could be described as individuals with PIMD according to the definition of Nakken and Vlaskamp (2007). While no test results were available for the participants, all the DSPs indicated that the developmental age of their clients was less than 24 months and that they experienced such serious constraints in terms of their motor skills that they were all confined to a wheelchair. In addition, 15 of the participants had epilepsy, 17 had been diagnosed with visual impairments and 4 had auditory impairments. An overview of the characteristics of the participants is provided in Table 1. Furthermore, for each client, one DSP who had known that client for at least 6 months had to be willing to participate in the study as well. Informed consent for participation in this study, including video registration, was obtained from their parents or legal representatives.

Table 1. Characteristics of the participants
 GenderAgeEpilepsyVisual impairmentAuditory impairment
 1Male10 × 
 2Male4 × 
 3Male16 × 
 4Female23 × 
 5Female13×× 
 6Male11×× 
 7Male47× ×
 8Male28×  
 9Male20×  
10Female31×× 
11Male49×× 
12Male10 × 
13Male10  ×
14Female12×× 
15Male10×× 
16Female13   
17Male15 × 
18Female6×  
19Female11×× 
20Female13×  
21Female13×××
22Female16 ××
23Female6×× 
24Female5×× 

Instruments

The Alertness Observation List (AOL; Vlaskamp et al. 2010) was used to determine alertness levels. The observation list distinguishes four levels of alertness, each of which is associated with a colour: (1) active, focused on the environment (green); (2) inactive, withdrawn (orange); (3) sleeping, drowsy (red); and (4) agitated, discontented (blue). Information recorded on four different forms was used to formulate an individual alertness profile. The overall description of each of the individual's alertness levels was supplemented with concrete examples of behaviour.

Previous research has shown that the AOL is a reliable instrument for determining alertness in individuals with PIMD. Both inter-observer and intra-observer agreement exceeded the 80% criterion when 78 videotapes of 23 children with PIMD were scored (Munde et al. 2011). While actual agreement about any one particular situation may depend on the knowledge of the DSP involved, the communicative skills of the individual with PIMD and the type of situation, it is always necessary to take all of these individual differences into account in any research on individuals with PIMD, and this can only come about through observations (Vlaskamp et al. 2010). Furthermore, DSPs have indicated that they find the AOL to be a useful instrument in clinical practice (Petitiaux et al. 2006).

Design

The present study was based on a quasi-experimental design. While the presentation of different stimuli in MSEs was the experimental condition, development of level of alertness was the dependent variable.

Procedure

In the present study, data were gathered in two steps. First, the AOL was completed for all participants. Second, at least three MSE sessions were videotaped for each participant. In these sessions, participants were offered stimuli during one-on-one interaction with a DSP. The DSPs were instructed to consider the individual's alertness profile when choosing stimuli. The choice of the type of stimulation as well as the presentation of the stimulus (such as the number of stimuli presented at the same time and the repetition of their presentation) had to be based on the abilities and preferences of the individual participant so that only those stimuli were included that were expected to be salient for the participant and therefore would increase the participant's alertness. Because the DSPs were free to discontinue the activity whenever they deemed it appropriate for the client, the length of sessions varied from 5 to 30 min.

A total of 76 MSE sessions were videotaped. Due to low quality of the recording or the absence of interaction with a DSP, 14 tapes were excluded from any further analyses. From the remaining pool of 62 tapes, one session for each participant (24 sessions in total) was selected at random and scored by one of the observers. Employing event sampling, the Media Coder (Bos & Steenbeek 2008) was the computer tool that facilitated the registration of the observations. Five tapes (20%) were scored by a second observer, employing the general agreement formula (Mudford et al. 1997). Inter-observer agreement was 86.2%. Based on the length of the shortest session (5 min) and to make all sessions comparable to each other despite the varying lengths, only the first 5 min after the start of the activity were included in the subsequent analyses.

The videotapes were scored by three observers. All of these observers were familiar with the aim of the study, and they had been trained in the use of the AOL. After attending a workshop to receive theoretical information about alertness in individuals with PIMD and the use of the AOL, the observers were asked to score some videotapes not included in the pool for the present study. Only when they had reached an inter-observer agreement of more than 80%, based on the general agreement formula (Mudford et al. 1997) for these trial videotapes, did they begin scoring the actual videotapes for the present study. Individual alertness profiles were employed as frameworks for determining alertness levels. For scoring purposes, the alertness level ‘alert’ was subdivided into two levels (i.e. ‘active alert’ and ‘passive alert’), in order to separate reactions including or excluding motor action. Based on previous analysis about the stimulation most frequently presented to individuals of the target group (Guess et al. 1999; Vlaskamp & Cuppen-Fonteine 2007; Vlaskamp et al. 2007), four kinds of stimuli were chosen to be scored in the MSEs: visual, auditory, tactile and vestibular. Depending on the preferences and abilities of the individual with PIMD, these could range from a bubble tube to a coloured stuffed animal for the visual stimuli and from music to the voice of the DSP for auditory stimuli. Examples of tactile and vestibular stimuli are receiving a massage and swinging in a hammock, respectively.

Analysis

The data were analysed employing time-window sequential analysis (Yoder & Tapp 2004). Sequential analysis in general reveals information about sequences in the behaviour observed. To investigate whether a given behaviour causes a target behaviour to occur more or less often than expected by chance, expected transitional probabilities are compared with observed transitional probabilities. In this way, the transitional probability is the chance that the target behaviour will occur relative to the given behaviour. The expected transitional probabilities are calculated based on the total number of possible event sequences and on the chance that one specific behaviour will occur in this sequence. For time-window sequential analysis, a time window is defined in relation to the given behaviour. The frequency of the target behaviour only in this time window is then included in the analysis. In a subsequent step, Yule's Q (Bakeman & Gottman 1997) shows whether the observed probabilities differ significantly from the expected ones. The value of Yule's Q can range from −1 to 1, whereby probabilities with a negative Yule's Q occur less often than expected by chance and probabilities with a positive Yule's Q occur more often. A Yule's Q of zero indicates no significant difference. Yule's Q can only be calculated when the marginal sums of the frequency table of the two behaviours are larger than 5 (Bakeman & Gottman 1997).

In the present study, the different alertness levels were treated as target behaviours and the four different kinds of stimuli as the given behaviours. Time windows of 10 s were defined in relation to each stimulus at a time, ranging from 0 to 10 s to 110 to 120 s. For each time window, the transitional probabilities for the different alertness levels were calculated. In addition, the values of Yule's Q were able to indicate whether the observed probabilities differed significantly from the observed ones. Because our aim was to determine overall tendencies and because marginal sums were low in the individual data, Yule's Qs were calculated for pooled data. In addition, we compared the individual data of some of those individuals who had sufficient marginal sums vis-à-vis the pooled data in order to check for the representativeness of our results.

Results

  1. Top of page
  2. Abstract
  3. Introduction
  4. Material and methods
  5. Results
  6. Discussion
  7. Key messages
  8. Acknowledgement
  9. References

Visual stimuli

Following visual stimuli, participants showed low percentages of ‘withdrawn’ or ‘asleep’ behaviour. These were always smaller than expected. While most participants were observed in the ‘active alert’ alertness level in the first 30 s after the stimulus, ‘passive alert’ behaviour also occurred more often than expected during the first 10 s. In the next 20 s (from seconds 30 to 50), participants were passively alert most of the time. After significant percentages for both ‘active alert’ and ‘passive alert’ behaviour from seconds 50 to 60, the highest percentages of the ‘active alert’ alertness level were observed again. Another similar change was observed for seconds 80 to 120, with significantly more ‘passive alert’ behaviour for the first 10 s and significantly more ‘active alert’ behaviour for the last 30 s.

The transitional probabilities for the visual stimuli are displayed in Table 2. Yule's Qs are included here to indicate whether the probabilities are lower or higher than expected by chance. In addition, the transitional probabilities are plotted in Fig. 1. The figure shows the development over time of the different alertness levels following visual stimuli.

figure

Figure 1. Plotted transitional probabilities of the different alertness levels following the visual stimuli.

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Table 2. Transitional probabilities and Yule's Q of the different alertness levels following the visual stimuli
TargetTime window (in seconds)
0–1010–2020–3030–4040–5050–6060–7070–8080–9090–100100–110110–120
  1. All transitional probabilities that have a Yule's Q > 0, thus higher than expected by chance, are marked in boldface.

Active alert 0.52 (0.70) 0.55 (0.72) 0.18 (0.05) 0.15 (−0.05)0.15 (−0.09) 0.24 (0.21) 0.29 (0.35) 0.18 (0.05) 0.10 (−0.29) 0.20 (0.11) 0.39 (0.53) 0.30 (0.37)
Passive alert 0.27 (0.21) 0.16 (−0.12)0.15 (−0.14) 0.21 (0.05) 0.35 (0.38) 0.22 (0.07) 0.01 (−0.93)0.06 (−0.56) 0.26 (0.20) 0.19 (−0.01)0.05 (−0.62)0.10 (−0.37)
Withdrawn0.000.000.05 (−0.04)0.000.000.000.000.000.03 (−0.38)0.000.000.00
Asleep0.000.000.000.000.000.000.000.000.000.000.000.00

Auditory stimuli (see Table 3 and Fig. 2)

figure

Figure 2. Plotted transitional probabilities of the different alertness levels following the auditory stimuli.

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Table 3. Transitional probabilities and Yule's Q of the different alertness levels following the auditory stimuli
TargetTime window (in seconds)
0–1010–2020–3030–4040–5050–6060–7070–8080–9090–100100–110110–120
  1. All transitional probabilities that have a Yule's Q > 0, thus higher than expected by chance, are marked in boldface.

Active alert 0.19 (0.09) 0.19 (0.08) 0.11 (−0.27)0.12 (−0.22)0.13 (−0.15)0.15 (−0.09) 0.24 (0.22) 0.21 (0.16) 0.17 (0.01) 0.11 (−0.23)0.13 (−0.13)0.15 (−0.06)
Passive alert 0.51 (0.64) 0.45 (0.57) 0.20 (0.03) 0.23 (0.11) 0.24 (0.15) 0.19 (−0.02)0.12 (−0.30)0.11 (−0.35)0.13 (−0.24)0.15 (−0.15)0.18 (−0.04)0.15 (−0.18)
Withdrawn 0.07 (0.06) 0.05 (−0.04) 0.06 (0.03) 0.09 (0.22) 0.08 (0.15) 0.14 (0.48) 0.15 (0.50) 0.03 (−0.42)0.01 (−0.80)0.02 (−0.55) 0.06 (0.03) 0.10 (0.31)
Asleep0.000.000.00 0.02 (0.33) 0.05 (0.76) 0.03 (0.54) 0.01 (0.14) 0.04 (0.66) 0.04 (0.63) 0.000.000.00

After the presentation of auditory stimuli, participants mostly showed ‘passive alert’ behaviour. Significant percentages of ‘active alert’ and ‘withdrawn’ behaviour were also observed in the first 20 s. While a substantial portion of the participants began to withdraw thereafter, there were others who even fell asleep after 30 s. Both behaviours were observed significantly more often than expected up until 120 s had passed. Seconds 90 to 100 form an exception to this; in this time window, no alertness level occurred more often than expected. Furthermore, large percentages of ‘active alert’ behaviour were found in seconds 60 to 90.

Tactile stimuli (see Table 4 and Fig. 3)

figure

Figure 3. Plotted transitional probabilities of the different alertness levels following the tactile stimuli.

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Table 4. Transitional probabilities and Yule's Q of the different alertness levels following the tactile stimuli
TargetTime window (in seconds)
0–1010–2020–3030–4040–5050–6060–7070–8080–9090–100100–110110–120
  1. All transitional probabilities that have a Yule's Q > 0, thus higher than expected by chance, are marked in boldface.

Active alert 0.23 (0.21) 0.23 (0.20) 0.06 (−0.53)0.07 (−0.49)0.07 (−0.45)0.12 (−0.20) 0.22 (0.16) 0.17 (0.01) 0.12 (−0.22)0.08 (−0.39)0.04 (−0.67)0.05 (−0.60)
Passive alert 0.36 (0.42) 0.30 (0.29) 0.21 (0.05) 0.26 (0.18) 0.28 (0.25) 0.26 (0.19) 0.12 (−0.27)0.17 (−0.07)0.18 (−0.05)0.15 (−0.18) 0.25 (0.17) 0.26 (0.20)
Withdrawn 0.17 (0.57) 0.13 (0.43) 0.04 (−0.21)0.05 (−0.12) 0.11 (0.35) 0.12 (0.38) 0.08 (0.15) 0.08 (0.20) 0.12 (0.41) 0.12 (0.41) 0.10 (0.28) 0.05 (−0.12)
Asleep0.000.000.00 0.01 (0.17) 0.000.00 0.04 (0.63) 0.04 (0.63) 0.000.000.000.00

The observed sequential relationship between the different alertness levels and tactile stimuli was similar to the one featuring auditory stimuli. In the first 20 s following the stimulation, participants showed significantly more ‘active alert’ and ‘passive alert’ behaviour than expected. In seconds 20 to 60, most of the observed behaviour was ‘passive alert’. Thereafter, high percentages of ‘withdrawn’ behaviour occurred in the time windows from 40 to 60 s as well. Starting at 60 s, participants were either actively alert or withdrawn or asleep. While in the following 30 s through to 110 s after the tactile stimulus, ‘withdrawn’ behaviour was the only behaviour that we observed significantly more often than expected, most of the participants were passively alert during seconds 100 to 120.

Vestibular stimuli (see Table 5 and Fig. 4)

figure

Figure 4. Plotted transitional probabilities of the different alertness levels following the vestibular stimuli.

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Table 5. Transitional probabilities and Yule's Q of the different alertness levels following the vestibular stimuli
TargetTime window (in seconds)
0–1010–2020–3030–4040–5050–6060–7070–8080–9090–100100–110110–120
  1. All transitional probabilities that have a Yule's Q > 0, thus higher than expected by chance, are marked in boldface.

Active alert0.16 (−0.04) 0.18 (0.05) 0.07 (−0.43)0.04 (−0.65)0.16 (−0.03) 0.30 (0.37) 0.25 (0.24) 0.02 (−0.83)0.13 (−0.16) 0.20 (0.11) 0.19 (0.08) 0.15 (−0.09)
Passive alert 0.37 (0.43) 0.21 (0.06) 0.23 (0.11) 0.27 (0.22) 0.18 (−0.06)0.06 (−0.56)0.02 (−0.86)0.13 (−0.25)0.17 (−0.07) 0.20 (0.02) 0.29 (0.26) 0.32 (0.32)
Withdrawn 0.09 (0.24) 0.10 (0.28) 0.07 (0.13) 0.03 (−0.29)0.03 (−0.39)0.04 (−0.25) 0.10 (0.29) 0.05 (−0.13)0.00 0.15 (0.50) 0.11 (0.33) 0.05 (−0.13)
Asleep0.000.000.000.000.00 0.05 (0.68) 0.10 (0.86) 0.06 (0.77) 0.000.000.000.00

In the time windows following vestibular stimuli, participants were mostly passively alert during the first 30 s after the stimulation. ‘Passive alert’ behaviour as well as ‘withdrawn’ behaviour occurred significantly more often than expected. Participants also showed significantly more ‘active alert’ behaviour in seconds 10 to 20 and ‘passive alert’ behaviour in seconds 30 to 40. In the 50 to 80 s following vestibular stimuli, most of the participants were either actively alert or asleep. After 90 s, significant occurrence of ‘active alert’, ‘passive alert’ and ‘withdrawn’ behaviour was observed. For the time windows at 40 to 50 s and 80 to 90 s, no alertness level was observed significantly more often than expected.

The results for all types of stimuli were similar for the pooled and individual data.

Discussion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Material and methods
  5. Results
  6. Discussion
  7. Key messages
  8. Acknowledgement
  9. References

To elaborate on our knowledge about the optimal combination of design and timing of activities for individuals with PIMD, the aim of the present study was to gather detailed information about the sequential relationship between different alertness levels and different stimuli as presented in an MSE. Looking at the time windows for each stimulus, different patterns became apparent. Following visual stimuli, alertness levels changed between ‘active alert’ and ‘passive alert’ in ‘waves’. Immediately following the stimulation, participants were mostly actively alert with two additional peaks at 50 to 80 and 90 to 120 s. In between, percentages for ‘passive alert’ behaviour were significantly higher than expected. These results confirm an approximate duration of alertness levels of 20 s, which has been found in previous studies as well (Guess et al. 1993; Mudford et al. 1997). In addition, the ‘waves’ might reflect an optimal alertness pattern for learning and development, because active engagement, on the one hand, and ‘passive alert’ behaviour, on the other, have been described as the most important alertness levels for effective learning (Guess et al. 1999). The sequential patterns following auditory and tactile stimuli showed a great many similarities. While most participants were either actively or passively alert in the first 20 s after the stimulation, only a small percentage showed ‘withdrawn’ behaviour and only during the first 10 s after the stimulation. Alertness then gradually decreased from seconds 20 to 120. While participants remained passively alert during seconds 20 to 50/60, they began and kept on withdrawing their attention, or even fell asleep, shortly thereafter. Those findings are again in line with previous studies that found that individuals of the target group can only remain alert for short moments (Guess et al. 1995; Mudford et al. 1997). However, the period of the ‘active alert’ alertness level in seconds 60 to 80/90 should be highlighted. It is quite possible that individuals with PIMD need time to process a stimulus displayed during ‘withdrawn’ or ‘passive alert’ behaviour before being able to be actively alert again. On the other hand, those who dislike the stimulus may also only show their reaction after about 60 s. Looking at the different patterns as a reaction to vestibular stimuli, the different nature of the stimuli might be an explanation. While ‘withdrawn’, ‘active alert’ and ‘passive alert’ alertness levels were observed immediately after the stimulation, reactions after approximately 1 min differed in two directions. One group of participants showed ‘active alert’ behaviour, whereas the other withdrew their attention from the stimulation. Because processing the vestibular stimulus might have taken the participants longer than it did the auditory or visual ones, the resulting reaction might not have been visible before the point 1 min after the stimulation (Barnett-Cowan & Harris 2009). Furthermore, the less explicit reaction at seconds 90/100 to 120 that was visible for all stimuli as another ‘wave’ of alert behaviour might be explained by the meaning of the different stimuli for the person with PIMD. While a stimulus can be salient and therefore meaningful for a person at the first moment it is presented, salience may decrease over the course of time (Mitchell & Le Pelley 2010).

Comparing the reactions with each other in terms of alertness to the different stimuli, we may conclude that visual stimuli are especially effective in promoting high alertness levels in individuals of the target group. It is striking that visual stimuli were never followed by ‘asleep’ alertness levels; percentages of ‘withdrawn’ behaviour only occurred during two time windows and were not significantly higher than expected. Additionally, visual stimuli were followed by large percentages of ‘active alert’ alertness levels, whereas the participants were passively alert most of the time after the presentation of auditory or tactile stimuli. Vestibular stimulation, in contrast, should be provided with utmost care. While previous studies have emphasized the importance of vestibular stimulation for motor and sensory development (MacLean & Baumeister 1982; Rues 1986), vestibular stimulation applied for a longer period and with higher frequency may result in a sensory overload (Ottenbacher 1982; Dunn 1997; Engel-Yeger et al. 2011). The difficulties that individuals with PIMD experience in processing information can cause them to become quickly overwhelmed when confronted with intense or unstructured stimuli. While some of the participants might have withdrawn their attention from those stimuli in order to express their dislike, others might have needed stimulation at a higher frequency to become active. Therefore, an individualized design for the activity when presenting vestibular stimuli is especially important.

A number of limitations should be noted when it comes to interpreting the results. Although this study was based on a quasi-experimental design, random selection was only carried out for the participants, and not for the stimulation. The choice of a stimulus and the way of interacting was different for each participant. Because individualized stimulation is especially important for individuals in the target group, DSPs were instructed to adapt their behaviour to the needs and abilities of the individual with PIMD. Consequently, stimuli were only comparable regarding their salience for the individual participant. Comparisons including similar stimuli or ways of interaction may reveal supplementary information. Furthermore, no chains of stimuli were taken into account. For example, the results produced by repetition may differ from those produced by presenting a stimulus only once. Moreover, the onset of several stimuli at the same time may well bring about different reactions. Because we did not control for additional stimuli that were presented after the first one in the subsequent time windows, these might also have influenced the reactions in terms of alertness levels. By including chains of stimuli in the analysis, future studies could reveal even more elaborated information. Because of the small sample size, not all conditions were present to an equal degree. The frequencies of some combinations were too low and thus had to be excluded from subsequent analysis. Future experimental studies that control for these aspects would certainly complement the present results.

The results of the present study reveal a number of important implications for the support and education of individuals with PIMD. Because all the stimuli were followed by high percentages of ‘active alert’ and ‘passive alert’ behaviour, we would suggest that DSPs always provide stimulation to make their clients alert. In both cases, adapting the stimulation to the individual's ‘waves’ would be necessary. The short period of the different alertness levels immediately after the stimulation shows that reactions in terms of changes in alertness levels are only slight. In addition, the ‘peak’ of alert behaviour after approximately 1 min emphasizes the ‘wave’ structure of those changes. As the DSPs continue providing stimulation, they should be able to observe whether a client is able to focus on the stimulus or whether he or she withdraws their attention. Only after a minimum of 90 s should the DSPs decide whether the stimulation is appropriate for the client. In short, DSPs need to take their time when observing alertness so as to ascertain the optimal timing of an activity for their clients. Following and stimulating the clients’ ‘waves’ of alertness thus becomes even more important. Based on such an approach, the long-term effects of supporting their clients, along with the educational activities vis-à-vis the cognitive and motor development of the individual concerned, could be fruitfully investigated in the future.

Key messages

  1. Top of page
  2. Abstract
  3. Introduction
  4. Material and methods
  5. Results
  6. Discussion
  7. Key messages
  8. Acknowledgement
  9. References
  • Detailed analyses of stimulation can inform researchers and direct support persons about optimal timing and duration of stimulation for individuals with profound intellectual and multiple disabilities. In this regard, visual stimuli are especially effective in promoting high alertness levels in individuals of the target group.
  • The present study found that the timing of changes in alertness following the stimulation differed due to different processing times for the different kinds of stimuli.
  • The waves of ‘active’ and ‘passive’ alertness may reflect an optimal pattern for learning and development for individuals with profound intellectual and multiple disabilities.

Acknowledgement

  1. Top of page
  2. Abstract
  3. Introduction
  4. Material and methods
  5. Results
  6. Discussion
  7. Key messages
  8. Acknowledgement
  9. References

We would like to express our special thanks to Roger Bakeman for his recommendations concerning the realization of the analyses.

References

  1. Top of page
  2. Abstract
  3. Introduction
  4. Material and methods
  5. Results
  6. Discussion
  7. Key messages
  8. Acknowledgement
  9. References
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