validating patient outcomes using an ipad-based software platform for language & cognitive...

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VALIDATING PATIENT OUTCOMES USING AN IPAD-BASED SOFTWARE

PLATFORM FOR LANGUAGE & COGNITIVE REHABILITATIONSwathi Kiran, Carrie Des Roches, Isabel Balachandran,

*Stephanie Keffer, Elsa Ascenso, *Anna KasdanSpeech and Hearing Sciences, Boston University

Department of Neurology, Massachusetts General Hospital

Funding from Wallace H. Coulter Foundation: BU-Coulter Translational Partnership Program

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Disclosure-Swathi Kiran

Has significant financial Interest• Chief Scientist for Constant Therapy• Ownership stock in Constant Therapy

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Other Authors: Carrie Des Roches, Isabel Balachandran, Elsa Ascenso- Nothing to disclose

Significant contributors- Stephanie Keffer, Anna Kasdan- Nothing to disclose

Disclosure

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Introduction• About 795,000 Americans each year suffer a new or

recurrent stroke (NIDCD.gov). Also, about 1.7 million individuals suffer from traumatic brain injury each year (CDC.gov).

• Individuals with language and cognitive deficits following brain damage likely require long-term rehabilitation.

• Consequently, it is a huge practical problem to provide the continued communication therapy that these individuals require.

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Using technology to improve treatment delivery

• Recent studies have examined the efficacy of rehabilitation techniques, such as videoconferencing, for individuals with hearing, stuttering and motor speech issues

• Other studies have provided aphasia therapy over the internet to individual patients

• More recently, there have several computerized brain-training software designed for normal adults.

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What is the evidence behind using technology to deliver treatment?

• CogMed• A software targeted at improving working memory abilities in

individuals with brain injury (Johansson & Tornmalm M, 2012; Lundqvist et al. 2012). These studies found improvements in working memory skills on the trained CogMed software as well as on other working memory tasks and functional settings.

• . Posit Science• Barnes et al (2009) examined the effectiveness of the software

Posit Science in improving auditory processing speed in individuals with mild cognitive impairment (MCI).

• Although differences between the experimental and control group were not statistically significant, verbal learning and memory measures were higher in the experimental group than the control group.

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What is the evidence behind using technology to deliver treatment? • Lumosity

• Finn and McDonald (2011) used Lumosity software to target attention, processing speed, visual memory in experimental and waitlisted controls. Results showed experimental participants improved on the training exercises more than the controls.

• There are other software programs- that function more like AAC- devices.

• Therefore, there an increased awareness and momentum for applying computer technology in the rehabilitation of aphasia

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Rationale• Additionally, there is increased patient demand to transition

from traditional but outdated flashcard based therapy in order to keep up with the evolution of technology.

• Nonetheless, the burden of evidence for technology-based treatment applications is no different than traditional treatment approach for rehabilitation after brain damage

• Q1. Can we provide a technologically based rehabilitation program that meets the same benchmarks for clinical efficacy?

• Q2. How do we individualize treatment for patients with brain damage as no two patients are alike?

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StudyQuestion: Does a structured therapy program that includes homework practice delivered through an IPAD result in significant gains in overall communication?

Goal: Compare patients who receive a structured IPAD delivered therapy program that is practiced up to 7 days a week with patients who receive standard one-on-one individualized therapy that is provided 1 or 2 days per week by a therapist.

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Study• Because of the flexibility that ipads provide to patients and

the accessibility to free/paid apps that provide variable levels of exercises, it is important to standardize the nature and form of treatment that is provided to patients using ipads.

• Since patients have access to ipads at home, it provides a unique opportunity to examine the extent of compliance when patients are provided with a homework regimen

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Participants

Experiment (N = 40) Control (N= 9)

Ave Age 63 years (SD = 11) 68 years (SD = 10)

Ave Months post Onset 54 months (SD = 47) 98 months (SD = 132)

WAB – AQ (western aphasia battery)

68.5 (SD = 26.52) 68.1 (SD = 31.35)

CLQT Composite Severity (cognitive linguistic quick test)

64% (SD = 25) 54% (SD = 28)

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Demographic data

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Experimental Design

Pre-Tx Assessment

Pre-Tx Assessment

Post -TxAssessment

Weeks

Post -TxAssessment

EXPERIMENTAL PATIENTS (N = 40)

CONTROL PATIENTS (N = 10)

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Sample therapies/assessments for language and cognitive processing

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Structure of the tasks- Language

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Structure of the tasks- Cognitive

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Individualized therapy assignment based on initial performance

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Reading Passages (Level 3)

Picture Ordering Tasks (5 items)Map Tasks (10 items, Level 2)

Syllable Identification (10 items)Picture Spelling (Level 3)

Word Identification

Word Copy (10 items)

Category Matching

Sound Identification (10 items)

Clock Tasks (10 items)

Week1 Week2 Week3 Week4 Week5 Week6 Week 7 Week 8 Week 9 Week 10Picture Spelling (Level 2)

Addition (Level 3)

Addition (Level 4)

Addition (Level 4)

Addition (Level 4)

Addition (Level 5)

Syllable Identification



Multiplication (Level 4)

Reading Passage (Level 2)

Picture Spelling (Level 3)








Picture Ordering Tasks (Level 4, 10 items)

Word Ordering (Level 2)

Subtraction (Level 3, 5 items)

Subtraction (Level 3)




Picture Ordering Tasks

Picture Ordering Tasks (Level 2)

Picture Ordering Tasks (Level 3) Division (Level 3)





Syllable Identification Division

Division (Level 2)

Clock Math (Level 2)




Multiplication (10 items)

Multiplication (Level 2, 5 items)

Word Matching (10 items)

Map Tasks (Level 2)

Map Tasks (Level 3)

Map Tasks (Level 3)

Word Ordering Task (Level 4)

Word Ordering Task (Level 5)

# 29 during week 6 homework # 25 during week 4 homework

# 44 for all 10 weeks

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Based on WAB, CLQT

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Low Language profile- Low cognitive profile

High Language profile- low cognitive profile

Low language profilehigh cognitive profile

High language profilehigh cognitive profile

86 year old male 75 year old male 77 year old male 56 year old male

• category matching• feature matching• picture naming • rhyming• sound

identification• word identification• sound to letter

matching • word copy • picture matching• symbol

cancellation

• picture spelling• naming picture• clock reading • instruction

sequencing• picture ordering • sound matching• symbol matching• voicemail

• category identification

• category matching • feature matching• letter to sound

matching • reading passage• sound

identification• sound to letter

matching • word copy• word spelling• word ordering

• category matching• feature matching• letter to sound

matching • sound to letter

matching • map reading• picture spelling• reading passage• rhyming • Sound• identification• syllable

identification• word spelling• word problems

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Carrie/• Please insert snapshots of the patient dashboard- that

shows start therapy, we will now do…

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Methods• During the weekly clinic sessions, the clinician would

decide to continue the participant on the same task or to modify the treatment plan based on his/her performance.

• If the participant achieved 95% or higher accuracy two times in succession,• The clinician would either progress the next level of difficulty (e.g.,

Addition Level 1 to Addition Level 2) • Would progress to a different task (e.g., assign category

identification after category matching).

• If participants performed at low accuracies or no change over several sessions, that therapy task was replaced with another task from the task list.

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Overview of data analysis1. Total therapy duration in weeks

2. Compliance- weekly log in times

3. Individual patient level analysis1. Analyze by week

2. Analyze by task

3. Analyze by item

4. Overall patient performance over time

5. Patient performance over time relative to population mean

6. Group level analysis: Analysis of tasks by patients

7. Group level analysis: Analysis of task by items, co-factors

8. Changes on standardized measures

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The average therapy period for controls was 12.964 weeks Average therapy period for experimental patients was 12.567 weeks

1 2 3 4 5 6 70

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Weeks of iPad therapy: experimental vs. control

Controls (n=7)

Experimental (n=36)

Patient

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1. Total therapy duration in weeks

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2. Compliance- Rates of log in to therapy

1/14/13

1/25/13

2/5/13

2/16/13

2/27/13

3/10/13

3/21/13

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Control patients Experimental patients

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Patient usage by week

Legend:

Control patients

Experimental patients

Idea

l Con

trol

904

698

882 52

Idea

l Exp

erim

enta

l81

395

583

1 25 29 47 527

913 24 68

3 28 81 335 59

1049 55

1 27 334 31 21

287

90

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12

1.00

7.00

Average therapy time per week

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3. Individual patient level analysis: By week

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3. Individual patient level analysis: By task

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3. Individual patient level analysis: By items

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0

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1

Patient #44

Addition LV3 Addition LV4

Addition LV5 Clock Math LV2

Division LV1 Division LV2

Division LV3 Division LV4

Map Reading LV2 Map Reading LV3

Multiplication LV1 Multiplication LV2

Multiplication LV3 Multiplication LV4

Multiplication LV5 Picture Ordering LV1

Picture Ordering LV2 Picture Ordering LV3

Picture Ordering LV4 Picture Ordering LV5

Picture Spelling LV2 Picture Spelling LV3

Picture Spelling LV4 Picture Spelling LV5

Reading Passage LV2 Reading Passage LV3

Subtraction LV3 Subtraction LV4

Subtraction LV5 Syllable Identification LV1

Voice Mail LV3 Word Matching LV1

Word Ordering LV2 Word Ordering LV3

Word Ordering LV4 Word Ordering LV5

Word Problem LV2 Word Problem LV3

4. Historical individual performance

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Time

0

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1

Patient #913

Addition LV4Clock Math LV2Clock Math LV3Division LV1Division LV2Long Reading Comprehension LV3Map Reading LV1Map Reading LV2Map Reading LV3Multiplication LV1Multiplication LV2Multiplication LV3Multiplication LV4Naming Picture LV1Picture Matching LV2Picture Matching LV3Picture Matching LV4Picture Ordering LV1Picture Ordering LV2Picture Ordering LV3Picture Ordering LV4Picture Ordering LV5Picture Spelling LV4Picture Spelling LV5Reading Passage LV3Rhyming LV1Subtraction LV2Subtraction LV3Subtraction LV4Word Problem LV1Word Problem LV2

4. Historical individual performance

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5. Patient performance over time relative to population mean

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6. Group level analysis: Analysis of tasks by patients- Individual Analysis Quantifying Change

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 210

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f(x) = 0.00497816883116883 x + 0.778570619047619R² = 0.0466686384257462

f(x) = − 0.236876103896104 x + 8.66886380952381R² = 0.476925453249178

Rhyming

Accuracy

Linear (Accuracy)

Latency

Linear (Latency)

Individual analysis: Quantifying Change

• With tasks that had an R2 value of above 0.25, the average of the first two sessions was subtracted from the average of the last two sessions

• Then each of those values was determined to be a “good” or a “bad” change• A “good” change in accuracy was any value above 0• A “good” change in latency was any value below 0

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Individual subject analysis by task Low Language profile- Low cognitive profile

High Language profile- low cognitive profile

Low language profilehigh cognitive profileHigh language profilehigh cognitive profile

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Group analysis of task improvementAverage % Significant (Overall) 19.63%% of 19.63% considered to be "Good" 79.62%

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100.00%

Percent of patients with significant R2 values by task for accuracy and latency

Percent of Significant R2 Percent of Significant considered "Good"

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ACCURACY


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Change in GOOD Significant R2 Accuracy (%)Average 14.45%Smallest Change 0.48%Largest Change 74.86%

Group analysis of task improvement

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LATENCY


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esChange in GOOD Significant R2 Latency (seconds)Average 0.144538506Largest Change 0.74861Smallest Change 0.004765

Group analysis of task improvement

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7. Group level analysis- co-factors

• Mixed regression models for each treatment task

• For e.g., for word identification

Overall effect of treatment is significant

Estimates for Accuracy Label Estimate SE DF t value Pr > |t|Conditional improvement 0.01961 0.0091 294 2.15 0.0325

Effect of WABAQ on improvement

-0.00066 0.0001 294 -4.88 <.0001

Effect of Composite Severity on improvement

0.01820 0.0137 294 1.32 0.1874

Task Accuracy LatencyEstimate p value Estimate P value

Category Identification -0.00633 0.5397 0.1135 0.569Category Matching -0.00019 0.9641 -0.05835 0.2064Feature Matching 0.02356 0.0034 -0.1878 0.0109Letter to sound matching Level 2 0.01018 0.294 -0.5329 0.0002Rhyming 0.01521 0.0032 -0.0223 0.6982Word spelling completion LV 1 0.01456 0.0187 -0.09176 0.3882Word Identification 0.01961 0.0325 0.1173 0.0183Addition Level 1 0.122 0.3455 -5.9979 0.0847Addition LV2 -0.05748 0.0307 2.8665 0.0093Addition LV3 -0.1141 0.1456 1.6905 0.1655Addition LV4 -0.1361 0.1438 -3.1877 0.0719Addition LV5 0.02881 0.1826 0.1198 0.8927Clock Reading Level 1 0.000816 0.9405 -0.4324 0.0016Clock Math Lv 2 -0.03675 0.9272 33.3335 0.0458Clock Math LV3 0.01032 0.9718Division LV1 0.05175 0.6368 -0.7804 0.7296Division LV4 0.48 0.9587 618.69 0.3172Instruction sequencing 0.03433 0.6964 4.6205 0.0971Map Reading LV1 0.2592 0.0014 0.09436 0.9073Map Reading LV2 -0.1268 0.1432 16.2842<.0001

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7. Can treatment outcome be predicted for each patient?

Solution for Random Effects

PatientId Estimate Std Err Pred DF t Value Pr > |t|23 -0.07216 0.2081 589 -0.35 0.728924 -0.5612 0.1757 589 -3.19 0.0015 903 0.4057 0.2544 589 1.59 0.1113913 0.3272 0.3258 589 1.00 0.3157955 0.08395 0.2471 589 0.34 0.7342978 1.0518 0.2442 589 4.31 <.00011049 -0.9511 0.1953 589 -4.87 <.00011079 -0.02336 0.2463 589 -0.09 0.92451091 -0.3460 0.2881 589 -1.20 0.23031339 0.4617 0.2044 589 2.26 0.0243

This patient show significantly less

improvement than the average improvement

This patient show significantly more improvement than

the average improvement

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8. Mean Changes on standardized test performance

LQ CQ AQ

Attent

ion

Mem

ory

Execu

tive

Funct

ions

Lang

uage

Visuos

patia

l Skil

ls

Compo

site

Sever

ity

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Drawing BNT

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Changes in standardized testing scores: experiemtnal vs. control

Average Control Change (n=9)Average TX Change (n=40)

Ave

rag

e C

han

ge

in S

core

(P

ost

-Pre

)

WAB CLQT BNT

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8. Change on standardized testsSubtest Experimental Group

(N= 40)Control Group (N = 9)

WAB-LQ 2.13% (t = -2.05, p <.05) 1.42% (t = 1.07, ns)

WAB-CQ 2.60% (t = -2.05, p <.05) 1.32% (t = 1.03, ns)

WAB-AQ 4.14% ( t = 3.11, p <.01) 0.65% (t = .76, ns)

CLQT-composite severity 9.12% (t = 3.28, p < .01) 4.44 % (t = .76, ns)

CLQT-Attention 11.3 % (t = -.4.55, p <.0001)

7.6% (t = -1.19, ns)

CLQT-Memory 1.55% (t = -0.84, ns) 1.140 (t = 0.30, ns)

CLQT-Executive Function 6.41% (t = 3.15, p < .01) 1.66 (t = 0.52, ns)

CLQT- Language 1.42% (t = 1.15, ns) 1.65 (t = 1.26, ns)

CLQT- Visuospatial skills 7.81 (t = 3.43, p < .001) 2.96 (t = 0.61, ns)

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Summary of results• Experimental and control patients completed 11 weeks of

treatment• Patient compliance was high-ranged from 1 time/week to 11

times/week• Patients who logged in more often showed more changes on

tasks assigned• Individual patient analysis by items, task, weekly- gives

insight into how patients perform• Changes in accuracy and latency are across tasks • Changes seen on standardized tests for experimental

patients, less for control patients

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THANK YOU ! Questions?

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References• Barnes D, Yaffe K, Belfor N, Jagust W, DeCarli C, Reed B, Kramer J.

Computer-Based Cognitive Training for Mild Cognitive Impairment: Results from a Pilot Randomized, Controlled Trial. Alzheimer Disease and Associated Disorders 2009; 23(3), 205-210.

• Finn M, McDonald S. Computerised cognitive training for older persons with mild cognitive impairment: A pilot study using a randomised controlled trial design. Brain Impairment 2011; 12(3), 187–199.

• Johansson B, Tornmalm M. Working memory training for patients with acquired brain injury: Effects in daily life. Scandanavian Journal of Occupational Therapy 2012; 19(2), 176-83. doi:10.3109/11038128.2011.603352.

• Lundqvist A, Grundström K, Samuelsson K, Rönnberg J Computerized training of working memory in a group of patients suffering from acquired brain injury. Brain Injury 2010; 24, 1173-1183. doi:10.3109/02699052.2010.498007

validating patient outcomes using an ipad-based software platform for language & cognitive...

Documents

disclosure slide

software programs

aphasia therapy

experimental group

computer technology

evolution of technology

used lumosity software

trained cogmed software