Katya Hill1,2 and Barry Romich1,3
1 University of Pittsburgh, Pittsburgh, PA 15260
2 Edinboro University, Edinboro, PA 16412
3 Prentke Romich Company, Wooster, OH 44691

Augmentative and alternative communication (AAC) evidence-based clinical practice requires quantitative measurement of communication performance. This paper presents a clinically useful report of summary measures based on automated data logging. These summary measures provide the quantitative data necessary for a structured and methodical approach to characterizing AAC performance. Together with traditional qualitative data collection procedures, AAC users and their facilitators will have a standardized report that provides comparable, compatible, and reliable statistical analysis of performance for guiding the therapy process and measuring outcomes.

Clinical evidence characterizing the performance of individual augmented communicators has been limited at best. Few AAC clinicians collect language samples (1). Even when they do, they seldom undertake quantitative analysis that would result in performance data. More frequent clinically, AAC practitioners have relied on traditional qualitative methods to collect evidence and make assessments of outcomes.

Automated logfile data and the clinical tools associated with language activity monitoring (LAM) have provided the field with innovative methods to gather language samples. A set of basic LAM tools, developed in part under a grant from NIH, is available for use with most text-based AAC systems (2) and a comprehensive universal logfile protocol has been defined and is implemented in at least one system (3). Samples collected with these tools can be edited, coded, and analyzed using a variety of automated and manual methods. The result is a set of summary measures that have proven useful in AAC clinical service delivery.

Studies are beginning to appear regarding the performance of augmented communicators using the LAM to collect data. These early studies have looked at the frequency distribution of the various language representation methods used by augmented communicators for spontaneous novel utterance generation (SNUG) (4, 5). One study reported on the use of the LAM with a young child using an AAC device to collect evidence on vocabulary use and early word combinations (6). Current trends with the development of performance monitoring tools and outcomes measurement would indicate continued growth in the reporting of similar studies.

Standardized assessment tools will make it easier to accumulate and compare aggregate outcomes across various parameters (7). A clinical protocol to report summary measures obtained from automated data logging provides a foundation to facilitate application of these tools for clinical decision-making, outcomes measurement, and research.

The product is a standardized summary measure report based on LAM data. The purpose is to have a systematic, principled approach to reporting summary measures that is comparable, compatible, and has reliable quantitative data for a variety of clinical applications. The LAM report can be used in conjunction with any additional qualitative data or assessment instruments collected clinically.

Operational procedures have been developed for five basic functions needed to generate the reporting protocol. The functions start with the uploading of raw LAM data and, based on the logfile, continue with the editing, coding, analysis, and report generation. Figure 1 represents that process involved in generating a LAM report.

Figure 1: Steps to convert LAM data to a summary measure report.

The LAM report header contains basic personal information on the subject, AAC device and selection technique information, sample date and time information, and the method of language sample generation. Specific summary measures included at this time are: A) Total number of utterances, B) Complete utterances as a percentage of total utterances, C) Spontaneous utterances as a percentage of total utterances, D) Mean Length of Utterance in words (MLUw), E) Mean Length of Utterance in morphemes (MLUm) (A morpheme is an element of meaning. Some words can have multiple morphemes.), F) Total number of words, G) Number of different word roots, H) Average communication rate (words per minute), I) Peak communication rate (words per minute), J) Selection rate (bits per second), K) Language Representation Method analysis, L) Word selection errors per utterance, M) Spelling errors per word spelled.

In addition to the summary measures, appended reports could include the following: 1) Raw LAM data, 2) Edited utterances, 3) Coded utterances, 4) Word list in alphabetical order, 5) Word list in frequency order, 6) Word list by Language Representation Method, 7) Word list comparison to reference lists.

The data is presented numerically and graphically using a bar chart. Up to three historic references are included with the current data to provide easy identification of trends. The summary measure report is a single sheet, both sides. Additional specific reports can be appended to the LAM Report as considered clinically useful. Figure 2 represents two sections of the report showing performance over time for the frequency of language representation method (LRM) use and word selection errors.

Figure 2: Typical presentation of data in the LAM Report.

A standardized report format will help to add structure to the work of the AAC clinician. It also will help to build a common foundation of information and knowledge that will facilitate the accumulation of evidence to support AAC clinical practice.

1) Beck, A.R. (1996). Language assessment methods for three age groups of children. Journal of Children's Communication Development. 17(2): p. 51-66.

2) Hill, K. & Romich B. (1999). AAC language activity monitoring and analysis for clinical intervention and research outcomes. C-SUN. Los Angeles, California.

3) Higginbotham, D.J., Lesher, G.W., & Moulton, B.J. (1999). Development of a voluntary standard format for augmentative communication device logfiles. in Proceedings of the RESNA '99 Annual Conference. Arlington, VA: RESNA Press.

4) Hill, K.J. & Romich, B.A. (1999b). Language activity monitoring for school-aged children:
Improving AAC intervention. American Speech-Language-Hearing (ASHA) Convention. San
Francisco, CA.

5) Hill, K.J. & Romich, B.A. (1999a). Identifying AAC language representation methods
used by persons with ALS. American Speech-Language-Hearing (ASHA) Convention. San
Francisco, CA.

6) Tullman, J. & Hurtubise, C. (2000) Language activity monitoring on a young child using a
VOCA. In Proceedings of the Ninth Biennial Conference of the International Society for
Augmentative and Alternative Communication. Washington DC.

7) Jutai, J., Ladak, N., Schuller, R., Naumann, S. & Wright, V., (1996). Outcomes measurement of assistive technologies: An institutional case study. Assistive Technology 8:110-120.

Katya J. Hill, M.A. CCC-slp
Edinboro University of PA
Edinboro, PA 16444
Tel: 814-734-2431
Fax: 814-723-2184
Email: khill@edinboro.edu