AJSLP

Research Article

Integrated Training for Aphasia: An Application of Part–Whole Learning to Treat Lexical Retrieval, Sentence Production, and Discourse-Level Communications in Three Cases of Nonfluent Aphasia Lisa Milman,a Mariana Vega-Mendoza,b and Deanna Clendenenb

Purpose: The purpose of this study was to evaluate integrated training for aphasia (ITA), a multicomponent language-production treatment based on part–whole learning that systematically trains lexical retrieval, sentence production, and discourse-level communications. Specific research objectives were to evaluate acquisition of target structures, statistical parameters associated with learning variables, treatment generalization, and the efficacy of individual treatment components. Method: ITA was administered to 3 individuals with nonfluent aphasia following a multiple-baseline, across-behaviors design. Effect size and correlational coefficients were computed to assess acquisition, generalization, and maintenance of target structures. Standardized tests and a treatment efficacy questionnaire were also completed.

Results: A significant treatment effect was found in 2 of the 3 participants. In addition, as is seen in normal skill acquisition, practice time and error rate were significantly correlated. All participants demonstrated evidence of generalization on standardized language measures. Only 1 participant improved, however, on the communication measures. Results of the treatment component analysis revealed significant differences in the perceived efficacy of individual therapy tasks. Conclusions: Findings add to the evidence supporting multicomponent aphasia treatments, provide preliminary support for ITA and the application of a part–whole learning approach, and suggest that specific treatment components may contribute differentially to outcomes and generalization effects.

M

including, for example, lexical treatments that target semantic and phonological (Cameron, Wambaugh, Wright, & Nessler, 2006), phonological and orthographic (Greenwood, Grassly, Hickin, & Best, 2010), or verbal and gestural (Rose & Douglas, 2008) levels of processing. Similarly, there are sentence-level treatments that target verb retrieval and aspects of morphosyntactic processing (Edmonds, Nadeau, & Kiran, 2009). Additional examples include discourse treatments that target word retrieval or sentence production within the context of communicative interactions (see Boyle, 2011, for review). There are several reasons for targeting multiple components of language processing within a single treatment program. First, from a basic neuroscience perspective, neuroimaging and behavioral research continue to reveal the extent to which linguistic networks are extensive and interconnected (Price, 2010). Studies of neurologically healthy individuals, for example, have shown that single word-priming effects may be modulated by contextual information provided in higher order sentential or discourse segments (see Ledoux,

ost research directed toward aphasia treatment targets a specific level of language processing. For instance, large bodies of literature are devoted to treatment of word retrieval (Boyle, 2004; Hillis, 1998), morphosyntactic processing (Schwartz, Saffran, Fink, Myers, & Martin, 1994; Thompson, Shapiro, Kiran, & Sobecks, 2003), discourse production (Basso, 2010; Chapman & Ulatowska, 1992), or social interactive aspects of communication (Elman & Bernstein-Ellis, 1999; Holland & Hinckley, 2002). Recently, however, we have noted a growing interest in developing therapies that target multiple components of language processing within a single treatment program,

a

Utah State University, Logan Ohio State University, Columbus

b

Correspondence to Lisa Milman: [email protected] Editor: Carol Scheffner Hammer Associate Editor: Heather Wright Received June 25, 2012 Revision received April 30, 2013 Accepted October 5, 2013 DOI: 10.1044/2014_AJSLP-12-0054

Key Words: aphasia, intervention, language, communication

Disclosure: The authors have declared that no competing interests existed at the time of publication.

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Camblin, Swaab, & Gordon, 2006, for a review). Further evidence supporting the facilitating effect of linguistic context comes from the aphasia treatment literature. Many classical treatment approaches rely on contextual or topic-focused cues to “stimulate” language (Duffy & Coelho, 2001; Morganstein & Certner-Smith, 2001). It is also well established that word retrieval can be facilitated by providing sentential information (Raymer & Kohen, 2006) as well as by providing phonemic (Cameron et al., 2006) or semantic (Boyle, 2004) cues. In fact, recent aphasia treatment studies have shown that treating multiple levels of language processing concurrently may have effects that are greater than those associated with independent treatment of isolated language impairments (Nickels, 2002). A second reason for training multiple processing levels is to facilitate generalization of treatment to everyday communicative interactions. Several studies have shown that training specific neurolinguistic impairments in isolation does not necessarily result in automatic or complete transfer to everyday communicative use (see review in Boyle, 2011), leading researchers to investigate whether generalization could be enhanced by training specific neurolinguistic targets in the context of more natural communicative interactions (Antonucci, 2009; Murray, Timberlake, & Eberle, 2007). Furthermore, most individuals with aphasia have multiple language impairments and receive an array of independent treatments (Goodglass & Menn, 1985). Treatment research that selectively targets multiple language components therefore has the potential to contribute new evidence as to how to optimally combine independent treatments and enhance clinical outcomes (see discussion in Schwartz, Fink, & Saffran, 1995). This study investigates integrated training for aphasia (ITA), a multicomponent treatment for spoken language production in aphasia based on part–whole learning (PWL; Knowles, Holton, & Swanson, 2005). PWL developed from two distinct approaches to learning. The first approach, part learning, is associated with the behaviorist tradition and programmed instruction. It reflects an analytical approach to training in which a particular skill is decomposed into smaller elemental parts and learned sequentially in a piece-bypiece manner. For instance, a part learning approach to teach paragliding might entail independent and sequential training of equipment, physics of flight, and maneuvering. The second approach, whole learning, is associated with gestalt and cognitive traditions. In this approach a particular skill is taught in its entirety as a single unit. To return to the previous example, using a whole learning approach, paragliding would be taught by getting in the harness and flying. All of the relevant component skills would be learned within the context of performing the complete task. Distinct advantages and disadvantages have been associated with both part learning and whole learning approaches (Cunningham, 1971; Hinckley, Patterson, & Carr, 2001). Advantages of part learning include (a) faster learning of component skills (because complex tasks are broken down into smaller simpler pieces), (b) positive transfer of learning between parts, and (c) greater generalization (because specific

cognitive skills are trained in isolation). On the other hand, advantages of whole learning include (a) greater associative learning (because there is increased opportunity to learn the relations between parts), (b) less negative transfer between parts, and (c) increased automaticity of situation specific responses. PWL integrates these two approaches into a single learning method. Complex tasks are divided into basic components that are learned independently (as in part learning) and collectively as a complete integrated task (as in whole learning). The relative strengths associated with using PWL have been examined in a variety of domains including perceptual–motor learning (Maas et al., 2008), procedural learning (Frederiksen & White, 1989), and second language acquisition (Bialystok, 1994). Several advantages have been linked to PWL as compared to either part learning or whole learning applied in isolation. A combined PWL approach has been linked to higher levels of performance on the complete task at the end of training (following the same amount of instruction), greater generalization to untrained (but related) tasks, faster acquisition times, and less variability in final scores for learners of different initial ability levels (Frederiksen & White, 1989). One factor that appears to be critical for the success of a PWL approach is the selection of component tasks targeted for training. Notably, tasks are selected based on normal (or expert) performance rather than individual learner–specific impairments (Frederiksen & White, 1989). This approach may seem counterintuitive for individuals with known neurological deficits. However, it is worth pointing out that in spite of hundreds of treatment studies aimed at specific neurolinguistic impairments, there is still no simple transparent relation between individual patterns of impairment and the success of a particular treatment (Nickels, 2002). A second important factor related to the success of a PWL approach centers on the particular sequence in which components are learned and then integrated into whole task performance (Cunningham, 1971). For example, in the pure part method, each component is learned as an independent unit in a fixed sequence before all components are combined to train performance on the whole task. In contrast, in the repetitive part method, Part 1 is taught in isolation; Parts 1 and 2 are taught together; and then Parts 1, 2, and 3 are taught together. Another variant is the progressive part method. In this method, learners are taught Part 1 in isolation; then Part 2 in isolation; then how to combine Parts 1 and 2; then Part 3 in isolation; then how to combine Parts 1, 2, and 3, and so on until all parts are learned. The decision as to how to decompose a particular cognitive task and how to sequence training is likely to be highly task specific. With respect to language instruction, the second language acquisition (SLA) literature offers some insight (Bialystok, 1994). Notably, SLA textbook chapters (see e.g., Van Patten, Lee, & Ballman, 2004) are typically organized around a set of component sections focused on vocabulary, grammar, and a variety of communicative activities (such as dialogues, conversations, and role play)

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that provide opportunities to practice vocabulary and grammar in more naturalistic and meaningful contexts. In general, these elements are instructed using a modified repetitive part sequencing schedule with independent but parallel training of vocabulary (in isolation), grammar combined with vocabulary, and communicative practice involving target vocabulary and grammar. Aside from a tutorial on learning approaches in aphasia (Hopper & Holland, 2005) and a comparison of part versus whole treatment approaches (Hinckley et al., 2001) there has been relatively little explicit discussion of a PWL approach in the aphasia treatment literature. Nonetheless, several recent studies have investigated elements of this approach. As stated above, several studies have examined the effects of treating two linguistic components (or parts) within the same treatment program (Cameron et al., 2006; Links, Hurkmans, & Bastiaanse, 2010). There have also been studies that have examined integrating single language components, either word retrieval (Peach & Reuter, 2010) or sentence production (Murray et al., 2007) into whole communication tasks. Finally, at least two studies have examined training multiple language components and integrating these multiple components into whole communicative tasks (Goral & Kempler, 2009; Springer, Willmes, & Haag, 1993). Results are largely consistent with findings from the more general PWL literature. Notably, particular performance patterns that have been associated with part versus whole learning in the more general cognitive science literature have also been documented in the aphasia treatment literature (Hinckley et al., 2001). Also, studies that have combined parts or integrated parts into whole communication tasks have been generally successful in terms of acquisition of the target structures and extension of treatment generalization to more naturalistic communicative contexts.

Integrated Training for Aphasia ITA is based largely on the second language acquisition PWL approach and a modified repetitive part sequencing schedule (Bialystok, 1994). As such, treatment includes independent but parallel training of lexical retrieval (in isolation), morphosyntactic processing (integrating target vocabulary), and discourse-level communications (integrating lexical and morphosyntactic training). Following other communicationbased approaches (Clausen & Beeson, 2003; Garrett, Staltari, & Moir, 1999; Morganstein & Certner-Smith, 2001), vocabulary items are selected by participants to maximize personal relevance, and treatment is focused on a particular semantic topic. In addition to one-on-one sessions, participants complete daily homework and attend a weekly group session, which provides an opportunity to practice target vocabulary and morphosyntactic structures in a conversational context.

Research Questions and Hypotheses The first objective was to evaluate whether administration of ITA to three participants with nonfluent aphasia would result in acquisition of trained structures, generalization to semantically related structures, and maintenance of training

effects over a 2-month period. A second objective was to more closely evaluate the learning–acquisition curves of participants. Specifically, we assessed whether there was a relationship between acquisition of targeted vocabulary and sentence production. We also tested whether there was a linear relation between hours of practice and error rate, as has been described previously for neurologically healthy adults acquiring a variety of complex multicomponent cognitive skills (Anderson, 1992). Our third objective was to determine whether training effects would generalize to improvements on standardized language and communication measures. A final objective was to conduct a treatment component analysis (based on participant, caregiver, and clinician ratings) to evaluate the perceived effectiveness of various ITA tasks. In view of the literature cited above and our pilot data, we predicted that participants would acquire and maintain the target structures and demonstrate generalization to related structures. In addition, we expected to see a significant correlation between acquisition of vocabulary and phrase structure as well as between hours of practice and performance accuracy. We also predicted that treatment effects would generalize to performance on standardized measures of language and communication. Last, we predicted that all components of ITA training would be rated as having approximately equal therapeutic value.

Method Participants Three monolingual English-speaking non-Hispanic White individuals with nonfluent aphasia participated in this study (see Table 1). The diagnosis of nonfluent aphasia was established based on medical history, performance patterns on the Western Aphasia Battery—Revised (WAB–R; Kertesz, 2007), and a clinical impression of effortful, simplified spoken language production in the context of relatively well-preserved language comprehension. Participants differed in the severity of their expressive language impairment. Participant 1 (P1; initial WAB–R aphasia quotient [AQ] = 26.4) and Participant 2 (P2; initial WAB–R AQ = 36.3) presented with more comprehensive and severe language-production deficits, affecting fluency, repetition, naming, and sentence production. The spontaneous speech of P1 was characterized Table 1. Participant demographic and medical history information. Variable

P1

P2

P3

Age (years) 66 55 66 Education (years) 16 14 16 Time postonset (months) 22 61 40 Handedness Right Right Right Lesion information L MCA CVA L MCA CVA L MCA CVA Gender F M F Note. P = participant; L MCA CVA = left middle cerebral artery cerebrovascular accident; F = female; M = male.

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by severe apraxia and minimal verbal output (mean length of utterance [MLU] = 0). Participant 2 had a mild to moderate apraxia of speech and produced only isolated nouns in his spontaneous speech (MLU = 1). Participant 3 (P3) presented with a moderate aphasia (initial WAB–R AQ = 78.5) and agrammatism but showed relatively preserved repetition and naming abilities. The spontaneous speech of P3 (MLU = 2.56) consisted largely of nouns, although occasional verbs, articles, and pronouns were also produced. Medical records indicated that all participants suffered a single stroke in the distribution of the left middle cerebral artery. At the time of the study, time postonset ranged from 2 to 5 years. Participant age ranged from 55 to 66 years and years of education ranged from 15 to 17 years. All participants were right handed and had no prior history of language, learning, psychiatric, or neurological impairment. In addition, participants passed a monaural pure-tone audiometric screening at 40 dB SPL at 500, 1000, and 2000 Hz.

Standardized Language, Communication, and Cognitive Testing The WAB–R, Communicative Effectiveness Index (CETI; Lomas et al., 1989), and Scales of Cognitive and Communicative Ability for NeuroRehabilitation (SCCAN; Milman et al., 2008) were administered pre- and posttreatment to assess language, communication, and general cognitive–communicative abilities (see Table 2). To further assess language-production abilities, we computed basic morphosyntactic measures (total number of meaningful words, total number of utterances, MLU, noun–verb ratio, and open–closed class ratio) using the language sample elicited from the WAB–R picnic scene. Additional discourse measures

were not administered because of the severe production deficits of P1 and P2.

Experimental Stimuli Core vocabulary. Color photographs of 65 daily activities (e.g., shopping, fishing) and 115 food items (e.g., tacos, ice cream) were taken using a Canon digital 350 Rebel XT camera and mounted on 4 × 6 index cards. To validate the iconicity of the stimuli, we asked 20 neurologically healthy individuals to name the items depicted in each photo. Any photos that were incorrectly identified by any member of the normative group were removed from the stimulus set and replaced by new photos that were subsequently named correctly or replaced until all photos were correctly identified. Participants with aphasia were then shown these stimuli in randomized order and were asked to nonverbally select 24 personally relevant activity items and 24 personally relevant food items. The 48 participant-selected items were used to create four balanced10-item sets (40 items total) that were used in treatment for each participant: Activity Set A, Activity Set B, Food Set A, and Food Set B. For each participant, the two sets within each semantic category were balanced for number of letters, number of syllables, frequency, imageability, and familiarity ratings (see online supplementary materials). Items that were not selected for treatment were either discarded or used as examples during training. Sentences. The 40 core vocabulary items selected by each participant were used to generate 40 individualized sentences. All stimuli were simple active, present tense, canonical sentences that began with (a) a first person singular pronoun (I ), (b) an auxiliary verb (am), and (c) a verb phrase in its gerund form (e.g., checking email, drinking coffee). Sentences

Table 2. Pre- and post-treatment performance on standardized measures of language, functional communication, and cognition. P1 Measure WAB–R AQ IC Fluency AVC Repetition Naming LSA Words Utterances MLU Nouns/verbs O/C class CETI SCCAN

P2

P3

Normative data

Pre

Post

Pre

Post

Pre

Post

Max

M

SD

SEM

26.4 3.0 2.0 7.5 0 0.7

31.8a 3.0 2.0 7.4 2.2b 1.3a

36.3 5.0 2.0 5.2 2.6 3.4

43.9b 7.0b 2.0 5.8 3.4a 3.8

78.5 9.0 4.0 9.4 8.4 8.5

82.3a 9.0 4.0 9.8 9.4b 9.0a

100 10 10 10 10 10

31.7 1.8 2.5 5.9 3.3 2.4

16.6 1.8 1.7 1.5 3.1 2.4

2.9 0.4 0.4 0.5 0.5 0.5

0 1 0 0 0 56.2 48

6 3 1.75b 5.b 0 55.1 48

5 7 1 0 0 36.8 45

12 8 1.5a 0 5b 33.0 41.a

19 9 2.6 3.3 2.8 53.3 69

30 9 3.9b 1.7b 2.3b 61.4b 79.b

— — — 100 100

4.5 1.5 2.2 68.0 59.8

1.6 0.7 1.2 16.8 15.8

0.3 0.1 0.2 4.1 3.2

Note. WAB–R = Western Aphasia Battery—Revised, normative data (for Motor/Broca’s aphasia) from the test manual (Kertesz, 2007); AQ = aphasia quotient; IC = information content; AVC = Auditory Verbal Comprehension; LSA = Language Sample Analysis, normative data (for aphasia group) from Thompson et al. (1995); O/C class = open–closed class words; CETI = Communicative Effectiveness Index, normative data (for stable patient group) from Lomas et al. (1989); SCCAN = Scales of Cognitive and Communicative Ability for Neurorehabilitation, normative data (for left hemisphere pathology group) from Milman (2003). Pre- to posttreatment change ≥1 SEM unit (65% confidence interval). bPre- to posttreatment change ≥ 2 SEM units (95% confidence interval).

a

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varied in length from three to six words. This variability was determined by the nature of the postverbal complement. Activity sentences ranged in length from three to six words and contained no complement (I am walking), a noun phrase complement (I am watching TV ), or a prepositional phrase complement (I am going to the movies). Food sentences varied in length from four to six words. All sentences contained either a simple noun phrase complement (I am eating melon) or a complex noun phrase complement (I am getting chips and salsa). The 40 sentences generated for each participant were divided into four 10-item sets as stated above. Within the parameters specified above, the phonetic and grammatical complexity of the sentences varied somewhat across individuals based on their language ability level. For example, for P1 food sentences were generated using only two verbs (eat, drink), for P2 sentences were generated with four verbs (eat, drink, get, have), and for P3 sentences were generated with five verbs (eat, drink, get, have, buy). Irrespective of these differences, for each participant, Set A and Set B sentences were balanced for phonetic complexity, sentence length, number of verb tokens, and postverbal complement type (see online supplementary materials).

Experimental Design A single-subject multiple baseline design across participants and behaviors was used to examine the effectiveness of ITA. Two consecutive treatment units were administered. Each treatment unit focused on training one of the four stimuli sets. The first treatment unit for all participants was Activity Set A. This was done to facilitate group language practice. However, the semantic topic of the second treatment unit was varied across individuals based on outcomes and generalization patterns during the first treatment unit.

Baseline Probe Procedures Baseline testing consisted of four testing blocks: naming food words (Sets A & B), generating food sentences (Sets A & B), naming activity words (Sets A & B), and generating activity sentences (Sets A & B). Within each of these four blocks, items were pseudorandomized so that no more than two items from Set A or Set B occurred consecutively. Naming probes. A 4 × 6-in. index card containing a photo of the target item was presented, and the participant was asked to identify the item. Two examples were provided before initiating testing. If the participant did not respond or provided an off-target response, a second prompt was provided: What food is this? (food items) or What activity is this? (activity items). Items were scored as correct if the participant correctly named the target item (or provided a plausible alternate response, e.g., sending email for checking email) within 30 s. Sentence probes. The same 4 × 6-in. index cards used for the naming probes were used to elicit sentences. The examiner presented the index card and asked the participant to use the photo to answer the question: What are you eating/ drinking? (food sentences) or What are you doing? (activity

sentences). Two examples were provided before initiating testing. If the participant did not respond or provided an offtarget response the examiner said, “Listen carefully to the question” and then repeated the question. Items were scored as correct if the participant accurately produced all target morphemes in the sentence (personal pronoun I, auxiliary verb am, main verb, and verb complement) within 30 s. Grammatically correct and semantically appropriate alternate responses (e.g., I am hitting a golf ball for I am golfing) were also scored as correct.

Treatment ITA was administered in three 60-min sessions per week. Each session targeted three levels of language processing: lexical retrieval (20 min vocabulary training), sentence production (20 min training of canonical pronoun + auxiliary verb + verb + -ing + verb complement sentences integrating the target vocabulary), and discourse-level interactions integrating the target vocabulary and target morphosyntactic structures (10 min scripted-dialogue training and 10 min of generative conversational practice). In addition, participants completed daily homework exercises focused on the target vocabulary and morphosyntactic structures. Finally, participants attended a 60-min group session once a week that provided additional opportunities to practice the target language structures in a conversational context. A description of the ITA treatment protocol is provided in the supplementary materials. Two treatment units were administered consecutively.

Treatment Probes Treatment probes were identical to baseline probes except that each probe was administered over two treatment sessions. One half of a probe (10 food words, 10 food sentences, 10 activity words, and 10 activity sentences) was administered at the start of each session. Probes were balanced so that each half probe contained an equal number of items from Set A and Set B. Treatment of a particular unit was discontinued once a participant scored 80% or higher on a minimum of two consecutive naming probes or two consecutive sentence probes. In some cases, treatment of a particular unit continued after criterion performance had been reached to further evaluate generalization to untrained items. Two follow-up probes were also administered at 1 month and 2 months following the completion of treatment.

Internal Validity Treatment records for each participant were reviewed by a second rater to ensure that all participants completed the full protocol within 60 min. Mean point-to-point agreement between specified and actual treatment procedures for the three participants was 91% (range = 85%–98%). The variability in treatment administration was largely because of P1 and P2 not completing the full protocol within 60 min during initial treatment sessions.

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Reliability Forty-five percent of the naming and sentence production probes were scored online by a second observer. Point-to-point agreement between the primary coder and the second observer for the production probes was 98% (range = 96%–99%). Disagreements in scoring were discussed and resolved based on consensus reached by the two scorers.

Analyses Percentage correct on daily probes was plotted graphically throughout the experiment to evaluate acquisition, generalization, and maintenance of target structures. Conservative dual-criteria (CDC; Fisher, Kelley, & Lomas, 2003) procedures were used to assist with visual interpretation of plots. Specifically, two interpretive lines (a line representing the baseline mean and a second trend line generated from baseline data) were plotted in the treatment phase (trained items) and corresponding generalization phase (untrained but semantically related items) of each graph. The two interpretive lines were then raised by an additional 0.25 SDs (in the direction of the expected treatment effect). Interpretation of any systematic change in performance resulting from intervention (for trained and untrained generalization items) was based on tallying the number of actual treatment data points located above both interpretive lines and comparing this number with a predetermined standard (Fisher et al., 2003). Effect size was also computed to determine the magnitude of change resulting from treatment. Because there was zero variability in some of the baseline measures, we followed recommendations made by Beeson and Robey (2006) and used the d2 statistic, in which variability is computed as the weighted average of variances for A1 (off ) and A2 (off ) treatment phases. Also, because we expected to see generalization across treatment sets, we used data from the initial baseline phase (before any treatment was initiated) for the A1 (pretreatment phase). Maintenance and follow-up data were used to compute the A2 (posttreatment phase). Pearson correlation coefficients were computed to determine whether there was (a) a relationship between acquisition of isolated vocabulary and acquisition of sentence structures and (b) a linear relation between hours of practice and error rate. Standard error of measurement was used to determine whether there was a statistically significant difference between pre- and posttreatment performance on standardized tests. Last, means and SDs of ratings made by participants, caregivers (who attended at least 30% of sessions), and clinicians were computed to evaluate the perceived efficacy of various treatment components.

Results Acquisition, Generalization, and Maintenance of Target Structures Performance on target vocabulary and sentence structures for the three participants is illustrated graphically in the Figures 1–3. Each figure depicts percentage correct during

baseline, treatment, maintenance, and follow-up phases for a single subject on the four sets (two trained, two untrained) of vocabulary and sentence structures. Baseline mean (long dash) and trend (short dash) lines are plotted in the treatment phase of each plot to assist with data interpretation. Note that in cases where the baseline mean equals zero, both interpretive lines (baseline mean and trend line) will overlap with the x axis and hence not be visible on the graph. Treatment effect size for each participant on trained items is shown in Table 3. Participant 1. Participant 1 was trained on Activity Set A and then on Food Set A. As shown in Figure 1, the first participant demonstrated a stable initial baseline and performed at or near floor level on word and sentence structures prior to initiating treatment. All data points in the treatment phase of trained structures (top four graphs) were above baseline mean and trend lines, indicating a systematic change associated with treatment. All target structures reached acquisition criterion (greater than 80% on words and/or sentences for two consecutive sessions) by the end of the treatment or maintenance phase. In general, performance on treated vocabulary items was closely tied to performance on treated sentence structures (Pearson r = .75, p < .01). However, improved performance on the vocabulary items (for both trained sets) preceded improved performance on the associated sentence structures. The overall weighted-mean treatment effect for words and sentences (as estimated by d2) was 7.10 (see Table 3). No overt generalization was observed from trained to untrained semantically related items. Maintenance of training on words and sentences after 2 months was at the same level or greater than immediately following treatment. Participant 2. Participant 2 was trained on Activity Set A and then Food Set A (see Figure 2). Inspection of baseline data suggests that P2 was highly stimulable on these tasks and demonstrated a gradually ascending baseline pattern throughout treatment, though his initial baseline data (first four probes prior to initiating any treatment) for all four sentence sets was at floor level. For trained single word stimuli (Activity Set A and Food Set A), although data points during the treatment phase for both sets were above baseline means (long dash lines), there was an insufficient number of data points above the trend line (0/20 for Activity Set A; 7/17 for Food Set A) to conclude that there was a systematic change from baseline performance resulting from treatment. In contrast, for the sentence data, an adequate number of data points (17/20 for Activity Set A; 12/17 for Food Set A) were above mean and trend lines, indicating that there was a systematic change in performance following treatment. All of the trained structures (words and sentences) reached acquisition criterion by the end of treatment or maintenance phase. As with P1, performance on treated vocabulary and sentence items was closely related (Pearson r = .66, p < .01). The overall weighted-mean treatment effect for words and sentences (as estimated by d2) was 8.22 (see Table 3). Using CDC interpretation procedures, no evidence of systematic semantic generalization was noted. Maintenance of training on words and sentences after 2 months was

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Figure 1. Participant 1 (P1): Treatment graphs. Percentage correct for P1 on baseline, treatment, maintenance, and follow-up (F/U) phases of words and sentences for trained (Activity Set A and Food Set A) and untrained (Activity Set B and Food Set B) items. Conservative dual-criteria (CDC) baseline mean (long dash) and trend (short dash) lines are plotted in the treatment phase of each graph to assist with visual interpretation. (Note that when the baseline mean = 0, CDC lines fall along the x-axis.)

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Figure 2. Participant 2 (P2): Treatment graphs. Percentage correct for P2 on baseline, treatment, maintenance, and F/U phases of words and sentences for trained (Activity Set A and Food Set A) and untrained (Activity Set B and Food Set B) items. CDC baseline mean (long dash) and trend (short dash) lines are plotted in the treatment phase of each graph to assist with visual interpretation. (Note that when the baseline mean = 0, CDC lines fall along the x-axis.)

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Figure 3. Participant 3 (P3): Treatment graphs. Percentage correct for P3 on baseline, treatment, maintenance, and F/U phases of words and sentences for trained (Activity Set A and Activity Set B) and untrained (Food Set A and Food Set B) items. CDC baseline mean (long dash) and trend (short dash) lines are plotted in the treatment phase of each graph to assist with visual interpretation.

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Table 3. Treatment effect size (d2) for the three participants on trained words and sentences. d2 P1

P2

P3

M

7.97b 8.11b,d 8.01b

7.39b 3.00 6.48a

2.10 3.39 2.53

5.82 4.83 5.67

5.84 7.30a,d 6.19a

6.69a 22.52c 9.96a

1.53 6.26a 3.11

4.69 12.03 6.42

8.22c

2.82a

6.05

Variable Words a. First trained set b. Second trained set c. Weighted mean for words Sentences d. First trained set e. Second trained set f. Weighted mean for sentences Overall (words and sentences combined) g. Weighted-mean treatment effect for words and sentences [(c + f)/2]

7.10c

Note. Effect sizes are designated using Beeson and Robey’s (2006) guidelines for lexical retrieval (Words), syntactic production (Sentences), and mixed treatments (Words and Sentences Combined). a Denotes small effect size. bDenotes medium effect size. cDenotes large effect size. d Treatment Set 1 used to compute variability due to zero variability in Treatment Set 2.

greater than 30% above baseline levels for both trained word and sentence sets. Participant 3. Participant 3 was trained on Activity Set A and then Activity Set B because of near-ceiling performance on both sets of food items during initial baseline testing (Figure 3). As with P2, P3 was highly stimulable on these tasks and had an ascending baseline on Activity Sets A and B for both words and sentences (see top four graphs). Nonetheless, treatment was initiated with the intent of stabilizing performance variability on the two activity sets and to determine whether stabilization of performance on these tasks would result in improved discourse production following treatment. For the first set of target words (Activity Set A), all data points in the treatment phase (8/8) were above baseline mean and trend lines, indicating a systematic change in performance as a result of treatment. In the remaining three graphs, we found an insufficient number of data points above the trend line to conclude that there was a systematic change compared with baseline performance. As with the other two participants, a close relationship between performance on words and sentences was revealed (Pearson r = .63, p < .01). For the two sets of trained activity items, improvements on vocabulary production preceded or surpassed improvements on sentence production. For example, on Activity Set A, P3 reached (and maintained) criterion level (80%) on single word stimuli immediately after treatment was initiated, whereas a criterion level of performance was not achieved on sentence stimuli until the fifth treatment session. Similarly, for Activity Set B, criterion was on the single word stimuli prior to initiating treatment, but not until the fourth treatment session (of Activity Set B) for sentence level stimuli. The overall mean-weighted treatment effect for P3 on words and sentences (as estimated by the d2 statistic) was 2.82 (see Table 3). Application of CDC procedures indicated that there was no systematic generalization to semantically related structures. Maintenance of performance on words and sentences after 2 months was greater than 30% above initial baseline levels for both sets of trained activity items.

Correlation of Hours of Practice With Error Rate Pearson correlation coefficients measuring the relation between hours of treatment and error rate are presented in Table 4. Ten of the 12 correlations demonstrated a significant negative linear relation between hours of treatment and error rate.

Pre- and Posttesting on Standardized Language Measures Results of pre- and posttesting are presented in Table 2. All three participants showed improvements on the WAB–R. Using conservative 95% confidence interval (CI) criteria on standard error of measurement (SEM) scores (1.96 × SEM; Harvill, 1991), P2 demonstrated significant gains on the WAB–R AQ and information content scores, whereas P1 and P3 demonstrated significant gains on the WAB–R Repetition score. With respect to the language sample analysis, all three participants produced a greater number of meaningful words. Using again 95% CI criteria on SEM scores (1.96 × SEM), P1 and P3 showed a significant increase on the mean number of morphemes produced per utterance (MLU, mean increase = 1.2 morphemes, SD = 0.5). Two of the three participants (P1 and P3) showed significant changes on the noun–verb ratio. In the case of P3, this value decreased (from 3.3 to 1.7), reflecting a more normal production pattern (mean for healthy controls = 1.5, SEM = 0.1). In contrast, P1 showed an increase in this value (from 0 to 5), reflecting a greater increase in production of nouns than in verbs. Similarly, two of the three participants (P2 and P3) showed a significant change in the open–closed class ratio. Again for P3 there was a decrease in this value (from 2.8 to 2.3), reflecting a more normal production pattern (mean for healthy controls = 2.2, SEM = 0.2). For P2, however, there was an increase in this value (from 0 to 5), reflecting a greater increase in production of open class than in closed class vocabulary.

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Table 4. Correlation of hours of practice with error rate. Variable Words Set 1 Set 2 Sentences Set 1 Set 2

P1

P2

P3

–0.82** –0.85**

–0.95** –0.66*

–0.91* –0.59

–0.95** –0.93**

0.81** –0.93**

–0.84* –0.40

of this approach, we administered ITA to three individuals with moderate to severe nonfluent aphasia. Results associated with each of our research questions are discussed below.

Acquisition and Maintenance of Target Structures

*p < .01, two-tailed. **p < .001, two-tailed.

For the two functional communication measures (CETI and SCCAN), application of 95% CI criteria on SEM scores indicate an increase for P3 on both measures and no change for P1 and P2 on either measure.

Treatment Component Analysis Ratings by participants, significant others, and clinicians of the relative effectiveness of the seven therapy components are presented in Table 5. Across these three groups, the overall mean rating (on a 5-point scale, where 1 = not helpful and 5 = very helpful ) for all therapy components was 4.3 (SD = 0.5). All other ratings were greater than 3.8 (within 1 SD of the mean), except ratings for computerbased homework training by participants (M = 3.3, SD = 1.5), significant others (M = 2.5, SD = 0.5), and clinicians (M = 3.8, SD = 1.3); ratings for dialogue training by participants (M = 3.7, SD = 0.8); and ratings for group therapy by participants (M = 3.0, SD = 0.7).

Discussion In this research, we evaluated the efficacy of ITA and more generally the applicability of a PWL approach to aphasia intervention. ITA systematically targets lexical retrieval, sentence production, and the integration of these two levels of language processing into more natural (whole) discourse-level communications. To investigate the efficacy

Our first hypothesis was that administration of ITA would result in acquisition of trained structures, generalization of training to semantically related structures, and maintenance of training effects over a 2-month period. This hypothesis was partially supported by our findings. All three participants acquired the trained target structures (words and sentences) by the end of treatment or maintenance phases. Furthermore, these changes in performance were largely maintained after a 2-month interval. Systematic generalization of training to semantically related but untrained structures, however, was not evident in any of the participants. The absence of a generalization effect for semantically related items may have been because of the relative complexity of the stimulus sets in this study. Notably, prior research has shown that generalization is maximized when moving in the direction from more complex (trained) items to less complex (untrained) items (Thompson et al., 2003). In this study, vocabulary lists were carefully balanced for complexity across a variety of dimensions. It may be that initiating training with more complex items would have resulted in greater generalization to semantically related (but less complex) items. Another possibility is that multicomponent treatments, targeting multiple levels of language processing, may promote greater generalization across language processing levels than within semantic categories. Further research is needed to investigate these two possibilities. For trained structures, the weighted-mean treatment effect ranged from small (P3) to large (P1 & P2). Further quantitative analysis of the treatment graphs using CDC procedures indicated that these changes in performance could only be attributed to treatment reliably in two of the three participants (P1 & P2). P3 was highly stimulable on both word and sentence tasks and demonstrated an ascending baseline pattern that could not be differentiated systematically from gains made as a result of treatment. Thus, the

Table 5. Ratings of individual therapy components.

Component Individual therapy Words Dialogue Sentences Conversation Homework Flash cards Computer Group Total M (SD)

Participants M (SD) (n = 3)

Significant others M (SD) (n = 2)

Clinicians M (SD) (n = 5)

Total M (SD) (n = 10)

4.5 (0.4) 5.0 (0.0) 3.7 (0.8) 5.0 (0.0) 5.0 (0.0) 4.3 (0.8) 4.3 (0.4) 3.3 (1.5) 3.0 (0.7) 4.2 (0.8)

5.0 (0.0) 5.0 (0.0) 5.0 (0.0) 4.5 (0.5) 5.0 (0.0) 5.0 (0.0) 5.0 (0.0) 2.5 (0.5) 5.0 (0.0) 4.6 (0.9)

5.0 (0.0) 4.2 (0.7) 4.0 (0.7) 4.4 (0.8) 4.3 (0.8) 4.0 (1.4) 4.3 (0.8) 3.8 (1.3) 5.0 (0.0) 4.3 (0.4)

4.8 (0.2) 4.7 (0.4) 4.2 (0.6) 4.6 (0.3) 4.8 (0.4) 4.4 (0.4) 4.5 (0.3) 3.2 (0.5) 4.3 (0.9) 4.3 (0.5)

Note. 1 = not helpful; 5 = very helpful.

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results for P3 are similar to previous studies (Nickels, 2002), which have found that simple repeated exposure to certain types of stimuli may lead to changes in performance that augment or equal treatment effects. Although all three participants acquired the target structures, they also differed with respect to their responsiveness to treatment in terms of both the magnitude of the treatment effect and acquisition time. The variability observed across participants was similar to that reported for other multicomponent aphasia treatment approaches (see e.g., Herbert, Best, Hickin, Howard, & Osborne, 2003; Springer et al., 1993) and was likely because of differences in initial severity level. For instance, P1, who had the most significant production impairment (initial AQ of 26.4) and began treatment at near floor level, took a relatively long time to acquire the target structures and showed a large treatment effect. In contrast, P3 had the best preserved language-production ability (initial AQ of 78.5), began the treatment program at a much higher level of performance (with much greater variability), took the least time to acquire the target structures, and did not show a reliable treatment effect on target structures.

Further Characterization of Acquisition Patterns To further characterize the performance patterns of participants, we also examined the relationship between acquisition of vocabulary and sentence structures as well as the relationship between hours of practice and error rate. With respect to the first analysis, we found a significant correlation in all three participants between acquisition of vocabulary and acquisition of sentence structures, indicating a close relationship between these two processing levels (see Figures 1–3). In most cases, acquisition of target vocabulary preceded and surpassed acquisition of target sentence structures. It is interesting to note, however, the reverse pattern was also observed. For instance, on both stimulus sets, P1 reached criterion on target sentence structures (requiring production of target vocabulary) before reaching criterion on production of the isolated vocabulary. This somewhat surprising finding is in line with sentence processing models that espouse feedback and feed-forward interactions between lexical retrieval and sentence formulation (Dell, 1986). It is also consistent with the facilitating effects of sentence closure tasks on word retrieval that have been demonstrated repeatedly in the aphasia literature (Raymer & Kohen, 2006). With respect to the second analysis, prior research has shown that neurologically healthy adults acquiring a variety of complex tasks show a negative linear relation between practice time and error rate (Anderson, 1992). We were interested in evaluating whether this same linear relation would be observed in persons with aphasia using a part–whole integrated approach to language intervention. In general, our data demonstrated a significant linear correlation between practice time and error rate (see Table 4). The only instance in which a linear relation was not observed was for P3 on the second set of words and sentences. In this particular instance, P3 was already at near-ceiling performance at the start of training the second set of items (>80% accurate on

words and sentences). Given this narrow range of score variation, it is not surprising that a correlation was not observed.

Generalization to Standardized Measures of Language and Communication A third objective of this research was to examine whether a treatment effect would also be evident on more general language and communication measures. All three participants showed significant increases in their pre- and posttreatment performance on multiple measures of connected speech. Specifically, significant changes were noted on MLU (P1 and P3), noun–verb ratio (P1 and P3), open–closed class ratio (P2 and P3), and WAB–R (P1, P2, and P3) scores. This generalization pattern is similar to that found in other multicomponent treatments that have reported pre- and posttreatment changes on comparable standardized measures (Edmonds et al., 2009; Links et al., 2010). However, changes on additional morphosyntactic variables, such as total number of verbs, verb diversity, and number of verb inflections, were not as extensive as in treatment studies that exclusively targeted verb production (Goral & Kempler, 2009) or morphosyntactic processing (Links et al., 2010; Murray et al., 2007). In contrast to the generalization observed on measures of connected speech, only P3 showed significant gains on the communicative measures (CETI and SCCAN). This outcome suggests that the language measures (as opposed to the more general communication measures) were more sensitive to the types of changes precipitated by ITA. Although these results indicate that ITA was not fully successful in facilitating transfer of acquired language skills to functional communications, anecdotal reports of significant others suggest that there was some generalization to daily contexts. For instance, as treatment progressed, the spouses of participants frequently reported hearing novel sentence productions (e.g., I’m hungry, I want ice cream, What are you doing?) that were beyond the participants’ pretreatment language ability level.

Treatment Component Analysis In general, participants rated treatment components as being of approximately equal value. There were three notable exceptions. First, among the four individual therapy tasks lower ratings were given to dialogue training than to vocabulary, sentence, or conversation training. This preference may have been because of differences in the type of learning associated with the four tasks. The dialogue task followed a script training protocol that used a more hard-wired automatic response that appeared to interfere with performance on the other more generative tasks. Notably, once participants had completed the dialogue training, their performance on the subsequent task was often affected by perseverations associated with the vocabulary and sentence structure from the memorized dialogue. With respect to homework training, there was also an overall (but not consistent) preference for hand-held flash

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cards over computer training. On the basis of raters’ comments, this preference was likely tied to prior experience and comfort level with computers. The participants and caregivers who did not regularly use computers outside of therapy found it to be less effective than other treatment components. In contrast, the one participant who used computers on a regular basis rated the computer homework as the most effective therapy component. Perhaps the most surprising finding from this analysis was that the individuals with aphasia rated one-on-one individual therapy and homework as significantly more effective than group treatment. In contrast, group treatment was rated as among the most effective therapeutic components by both clinicians and significant others. This result replicates findings from recent studies, which have also shown discrepancies between therapeutic priorities set by persons with aphasia versus significant others or clinicians (Cranfill & Wright, 2010). Specifically, the low ratings given to group therapy by persons with aphasia are consistent with findings from a recent Swedish survey of 172 individuals with aphasia (Johansson, Carlsson, Östberg, & Sonnander, 2012). According to the results of the survey, significantly more respondents rated language ability as important (87%) than functional communication training (51%) or psychosocial interventions (14%). The authors of the survey suggested that these differences may reflect a limited understanding of the purpose of alternate intervention forms. The explanatory comments offered by our participants are largely consistent with this position. For instance, one participant communicated that he had more opportunities to talk with the clinician during the one-on-one sessions. Thus, it may be that goals of group treatment were not as obvious to participants as were the goals of other therapeutic components and therefore may require additional explanation prior to initiating treatment. Alternatively, it may be the case that these groups simply have different priorities.

Part–Whole Learning and Integrated Training for Aphasia This research provides preliminary support for the application of a PWL model to aphasia treatment. One unique feature of the PWL model is that treatment targets are selected based on normal–expert performance rather than patient-specific impairments. In this study components were selected based on a model of typical adult language learning. Although all three participants had deficits in spoken language production, they differed with respect to the severity of their impairment on the component tasks. In particular, P3 demonstrated relatively well-preserved word finding ability and showed a high level of performance prior to the onset of intervention. In fact, we were unable to establish a stable baseline and did not see a reliable treatment effect in this participant. In spite of these differences in performance, all three participants performed similarly with respect to acquisition of target structures, generalization patterns, and rate of recovery. Furthermore, although P3 did not show a significant treatment effect on target structures, P3 was the only

participant to show a change in standardized measures of functional communication. In addition, it is interesting that irrespective of differences in performance, participants, significant others, and clinicians were highly consistent in their retrospective ratings of the value of individual treatment components. For instance, all three participants gave identical ratings for the value of word, sentence, and conversation treatment components. Collectively, these results suggest that patients who differ in their initial ability on component tasks may still benefit from the same PWL treatment program but are likely to show somewhat distinctive patient-specific recovery patterns.

Implications for Clinical Practice Results of this research have several implications for clinical practice. First, although it is widely recognized that intensity is likely to be a factor that contributes to aphasia treatment success (Robey, 1998), little is known about exactly what is meant by intensity. For instance, should intensity focus on a single task or on multiple tasks targeting several levels of language processing simultaneously? The results presented here add to a growing body of evidence supporting a multitiered approach to intervention. The close relationship and facilitating effects observed across levels of language processing suggest that a linear treatment sequence, beginning with isolated training of a single task, such as word retrieval, may not always be the most effective or efficient treatment strategy. This is likely to be particularly true when the goal of intervention is to promote changes in real world language use. Second, that individuals with aphasia in this study and others (Hinckley et al., 2001) have responded to part–whole learning techniques in similar ways to other neurologically healthy adults learning complex tasks has further implications for clinical practice. Notably, the existence of a linear relationship between practice time and error rate could be used by clinicians to predict future performance based on initial ability and hours of therapy–practice. In this regard, our results complement those of Hickin, Best, Herbert, Howard, and Osborne (2002), who also found that long-term treatment outcomes could be predicted based on initial patterns of performance. A further consideration relevant to clinical practice centers on the variability observed across participants in this study and in other multicomponent approaches. In some cases, the learning appears to be more narrowly focused and relatively item specific, whereas in other cases there appears to be a more generalized response that extends more broadly to measures of language and functional communication. Administering a comprehensive pre- and posttreatment assessment battery that samples item-specific responses, language, and functional communication would allow clinicians to capture the full range of possible recovery patterns.

Limitations and Future Directions Although the results of this study are promising, clearly much more research is needed to replicate these findings on a

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larger sample of individuals and to further explore variability in individual performance patterns. The success of any PWL approach depends largely on the components (parts) selected for treatment and the way in which components are combined to facilitate integrated whole task performance. This research represents the first step in an iterative process of identifying the optimal tasks and treatment schedule to maximize a PWL approach for treating spoken languageproduction training in aphasia. As stated above, results of the component analysis indicated that participants, significant others, and clinicians found it helpful to combine word retrieval, sentence processing, and conversational practice into a comprehensive treatment program. However, our results also suggested that not all treatment components were of equal value in this program. Much more research is needed, therefore, to identify the component tasks that will result in the greatest treatment success. In addition, little is known about how to optimally combine individual treatment parts. In this study, we followed the SLA literature and applied a modified repetitive part training schedule. An important goal for future research will be to compare performance accuracy and acquisition times associated with alternate treatment schedules. For example, should different treatment components be taught in parallel (as was done in this study) or with a more traditional sequential schedule? If components are trained sequentially, what decision criteria should be used to advance to the next level of training? Finally, more research is needed to identify which specific components and training schedules will ultimately trigger the greatest carryover to everyday communicative activities. Addressing these questions will advance our understanding of how to most effectively treat individuals with multifaceted language impairments and diverse communication needs.

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