Rupa Patel's Ph.D. Dissertation Defense, UW Biomedical & Health Informatics

Designing for Use and Acceptance of Tracking Tools in Cancer Care

Rupa PatelBiomedical & Health InformaticsOral Dissertation DefenseAugust 6, 2013

Self-Tracking in Cancer Care

MOTIVATION: Why Track Symptoms in Cancer

Care?

AIM 1: ePRO Tool Use and Symptom Distress

AIM 2: Patient-Driven Self-Tracking

MODEL: Design Considerations for Tracking Tools

CONTRIBUTION & FUTURE WORK2

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MOTIVATION: Why Track Symptoms in Cancer

Care?

AIM 1: ePRO Tool Use and Symptom Distress

AIM 2: Patient-Driven Self-Tracking

MODEL: Design Considerations for Tracking Tools

CONTRIBUTION & FUTURE WORK3

DIS

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Self-Tracking in Cancer Care

4

MO

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NMarypatient withbreast cancer

Thumb infection

Insomnia

Pain

Unstable glucose level

Weight loss

Fatigue & Nausea

Hives Dry cough

High blood pressureSwelling

5

Symptom Communication

6

Nausea

Fatigue

Pain

Weight loss

MO

TIV

ATIO

N

{ CLINIC VISIT }

7

MO

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N

Nausea

Fatigue

Pain

Weight loss

Swelling Neuropathy

Symptom Communication

{ CLINIC VISIT }

8

Thumb infection

Vaginal dryness

Dry cough

Anxiety

..

MO

TIV

ATIO

N

Nausea

Fatigue

Pain

Weight loss

Swelling Neuropathy

Symptom Communication

{ CLINIC VISIT }

• Care based on continuous healing relationships

• Shared knowledge and the free flow of information

• Personalization based on patient needs and values

9

Institute of Medicine (IOM) Report, 2001

MO

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N

Communication Needs in Cancer Care

Can Tracking Tools Help?

10

MO

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N

Cooking Hacks Website http://www.cooking-hacks.com/index.php/documentation/tutorials/ehealth-biometric-sensor-platform-arduino-raspberry-pi-medical

Researcher/Clinician-Driven

• Patient-Reported Outcome

• Ecological Momentary Assessment

Patient-Driven

• Personal Informatics Self-Tracking

11

MO

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N

Tracking Tools Across Fields

PRO

EMA

PInf

Abernethy, AP et al. (2008). Health Serv Res 43(6): 1975-91.

Patient-Reported Outcome Instrument: FACT-G

12

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N

Cella et al, Journal of Clinical Oncology, 1993

PRO

ESRA-C2

13

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N

Symptom Assessment

Report View

Teaching TipsBerry, ASCO 2012.

PRO

Benefits of Patient-Reported Outcome Tools

• Improved health outcomes1,2

• Clinician awareness of symptoms3,4

• Timing of symptom reporting5

14

MO

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N

1Velikova, Journal of Clinical Oncology 2004; 2Detmar JAMA 20023Berry, Journal of Clinical Oncology 20114Ruland, JAMIA 20105Cleeland, Journal of Clinical Oncology 2011

PRO

Ecological Momentary Assessment

• Study of human behavior in daily life

• Random or periodic reminders to track

15Stone, Shifman, et al“The Science of Real-time Data Capture” 2007

MO

TIV

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N

EMA

Benefits of Ecological Momentary Assessment Tools

• Limited recency effects

• Improved event recall

• Capture of mood and context

16

MO

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N

Stone, Shifman, et al“The Science of Real-time Data Capture” 2007

EMA

• Patient-initiated tracking

• One generates data for personal insight and action

17

MO

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N

Personal InformaticsPInf

Benefits of Personal Informatics Self-Tracking

• Clinic adoption not needed

• Wide selection of apps and devices

• Consumer-facing interface design

18

MO

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N

PInf

Barriers to Tracking Tool Use

19

MO

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N

Retrospective recall X

Data integration X X

User burden X X X

Interruptions X

Interpretation of meaning

X X

PInfPRO EMA

Donaldson, Quality of Life Research, 2008

Stone, Shifman, et al. “The Science of Real-time Data Capture,”

2007

Li, et al. CHI 2010

U.S. Symptom Tracking Habits

7 in 10 adults track a health indicator• 49% of trackers keep track “in their heads”• 34% of trackers track on paper• 21% of trackers use technology

Pew Study: Tracking For Health, January 2013

Rural patients with cancer or survivors (n=134)• 1 in 3 tracked health issues during treatment• 1 in 11 used technology to track health data

Hermansen-Kobulnicky et al. Support in Cancer, 200920

MO

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N

21

How do we design tracking tools that are used and

accepted by both patients and clinicians in standard cancer

care?

MO

TIV

ATIO

N

Self-Tracking in Cancer Care

MOTIVATION: Why Track Symptoms in Cancer

Care?

AIM 1: ePRO Tool Use and Symptom Distress

AIM 2: Patient-Driven Self-Tracking

MODEL: Design Considerations for Tracking Tools

CONTRIBUTION & FUTURE WORK22

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Aim 1 Research Questions

1.1 How often do patients with cancer voluntarily use an ePRO tool?

1.2 Is frequent voluntary use of an ePRO tool associated with a reduction in symptom distress of patients with cancer?

23

AIM

1: e

PR

O T

OO

L USE

Data: Intervention Group (n=372) from Randomized Controlled TrialIntervention• Voluntary access to ESRA-C2 ePRO

Assessment-taking sessions at any time• Access to Teaching Tips/Report Views at Study

Time PointsInclusion criteria• Any cancer• Enrollment prior to treatment start• Adults 18+

24

AIM

1: e

PR

O T

OO

L USE

Berry, ASCO 2012

ESRA-C2

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AIM

1: e

PR

O T

OO

L USE

Symptom Assessment

Report View

Teaching TipsBerry, ASCO 2012

Symptom Assessments in ESRA-C2Questionnaires• Symptom Distress (SDS15)• Depression (PHQ-9)• Quality of Life (EORCTC-QLQ-30)• Chemotherapy-induced neuropathy (EORCTC-QLQ-

CIPN30)• Skin changes• Fever/chills• Sex-related symptoms• Patient prioritization

77 total questions at study time points30 total Symptom & Quality of Life Issues (SQLI) 26

AIM

1: e

PR

O T

OO

L USE

Berry, ASCO 2012

Data Collection Procedures

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AIM

1: e

PR

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OO

L USE

Study Time Points• Symptom assessment

• Reminder to take assessment

• Clinician receives report

Intervention: Access outside of 4 study time points• Choice of symptom assessments

• Viewing reports

• Viewing teaching tips

Reminder phone call 1 week after enrollment at S1

Consult prior to

treatment

S1 First on-treatmen

t VisitS2

6-8 weeks after

treatment start

S32-4

weeks after

treatment end date

S4

Berry, ASCO 2012

RQ 1.1 Analysis Methods

How often do patients with cancer voluntarily use an ePRO tool?

• Descriptive statistics

• Contingency table / Fisher’s Exact Test

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AIM

1: e

PR

O T

OO

L USE

Overall Use of ESRA-C2

29

AIM

1: e

PR

O T

OO

L USE

S1

V1.1

V1.2

V1.3

V1.4

V1.5

V1.6 S2

V2.1

V2.2

V2.3 S3

V3.1

V3.2

V3.3 S4

V4.1

0

100

200

300

400

Frequency

of

Sess

ions

S1 S2 S3 S4

Study Time Points Voluntary Sessions

First on-treatmen

t Visit

S2

6-8 weeks after

treatment start

S3 2-4

weeks after

treatment end date

S4

Consult prior to

treatment

S1

S v

Time Points over the Course of the Study

Voluntary Sessions Are Less Likely to Include Completed Symptom Distress (SDS15) Assessments

Fisher’s exact test (p < .001)

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AIM

1: e

PR

O T

OO

L USE

S

v

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AIM

1: e

PR

O T

OO

L USE

RQ 1.2 AnalysisIs frequent voluntary use of an ePRO tool associated with a reduction in symptom distress of patients with cancer?

One-way between-group ANOVA

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AIM

1: e

PR

O T

OO

L USEDependent Variable

• SDS15 score• Range: 15 - 60

Independent Variable• Voluntary Use• 3 levels: 0, 1, ≥2 uses

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AIM

1: e

PR

O T

OO

L USE

RQ 1.2 AnalysisIs frequent voluntary use of an ePRO tool associated with a reduction in symptom distress of patients with cancer?

One-way between-group ANOVA

Frequent users (≥2 uses) had significantly lower end-of-study symptom distress scores than those with just 1 use (p < .05)

32

AIM

1: e

PR

O T

OO

L USEDependent Variable

• SDS15 score• Range: 15 - 60

Independent Variable• Voluntary Use• 3 levels: 0, 1, ≥2 uses

Symptom Distress, by Use GroupA

IM 1

: ePR

O T

OO

L USE

S1 S2 S3 S420

21

22

23

24

25

26

27

28

29

30

No Use (n=123) 1 Use (n=92) ≥2 Uses (n=74)

Sym

pto

m D

istr

ess

(SD

S15

Sco

re)

S1 v S2 v S3 v S4

Study Time Points

Voluntary Sessions

S

v

Time Points over the Course of the Study

33

Aim 1 Summary

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AIM

1: e

PR

O T

OO

L USE

• Low overall voluntary use of ePRO tool

• Frequent users had lower end-of-study symptom distress than those with 1 use

• Future work to identify reasons for RCT effect

Limitations

• No data on acceptability of features

• Varied length of treatment

• Focus on a general symptom measure

35

AIM

1: e

PR

O T

OO

L USE

Self-Tracking in Cancer Care

MOTIVATION: Why Track Symptoms in Cancer

Care?

AIM 1: ePRO Tool Use and Symptom Distress

AIM 2: Patient-Driven Self-Tracking

MODEL: Design Considerations for Tracking Tools

CONTRIBUTION & FUTURE WORK36

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Aim 2 Research Questions

2.1 What are barriers to self-tracking during cancer care?

2.2 How does actual use of tracking tools benefit patients?

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AIM

2: PA

TIE

NT T

RA

CK

ING

Data Collection Methods

“In-the-Wild” Field Study (n=15)• home & clinic

observations• interviews• questionnaires

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AIM

2: PA

TIE

NT T

RA

CK

ING

“Technology Probe” Study (n=10)• tool use logs• clinic observations• interviews• questionnaires

Inclusion criteria: Women with breast cancer

Unruh et al, CHI 2010 Klasnja et al, CHI 2010

HealthWeaver “Check-in” Entry

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Web Mobile

AIM

2: PA

TIE

NT T

RA

CK

ING

HealthWeaver Graphing

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AIM

2: PA

TIE

NT T

RA

CK

ING

Open Coding Analysis Themes

• Health issues & metrics• E.g., nausea, anxiety

• Tracking behavior• E.g., sporadically in notebooks

• Barriers to self-tracking in the wild

• Benefits of self-tracking with HealthWeaver

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AIM

2: PA

TIE

NT T

RA

CK

ING

Findings: Tracking with cancer

Barriers “in the Wild”• Limited clinical guidance• Fragmentation of data• Time & energy burden

Benefits with HealthWeaver• Augmented memory• Psychosocial comfort• Communication support with clinicians

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AIM

2: PA

TIE

NT T

RA

CK

ING

Patel et al., AMIA 2012

Barrier: Limited Clinical Guidance• Patients use memory to recall symptoms

• Clinicians recommend few metrics to track

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P8’s drain log

AIM

2: PA

TIE

NT T

RA

CK

ING

Barrier: Fragmentation of Data

• Paper, MS Office used to track

• Difficult to reflect

• Data unified by just 1 participant

44P9’s notebook

AIM

2: PA

TIE

NT T

RA

CK

ING

HealthWeaver Tracking Usage

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Metrics required for study

Default metrics in HealthWeaver

Average metrics tracked = 8.8(n=10)

AIM

2: PA

TIE

NT T

RA

CK

ING

Benefit: Augmenting Patterns

P19: “So I was able to look back and see, I wasn’t feeling this bad, what’s going on now?”

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memory

support

AIM

2: PA

TIE

NT T

RA

CK

ING

Benefit: Communication Support with Clinicians

P17: “I was able to show [my doctor] that my hip was getting worse over time and that she should take it a little more seriously, [given] the fact I had it for day after day after day and I could show her what was going on.”

47

AIM

2: PA

TIE

NT T

RA

CK

ING

Patient priorities & data

Benefit: Psychosocial Comfort

P23: “…[documenting] something good that happened, any new news, and good news, might be helpful to go back and remember that there have been improvements.”

48

AIM

2: PA

TIE

NT T

RA

CK

ING

Design Implications

• Provide pre-populated metrics

• Provide customizable metrics

• Facilitate reflection and communication with clinicians

• Support patient ownership of tracking process

49Patel et al, AMIA 2012

AIM

2: PA

TIE

NT T

RA

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Aim 2 Summary

• High use of personal informatics tracking tool

• Unexpected benefits of self-tracking

• Design implications drawn from benefits and barriers

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AIM

2: PA

TIE

NT T

RA

CK

ING

Self-Tracking in Cancer Care

MOTIVATION: Why Track Symptoms in Cancer

Care?

AIM 1: ePRO Tool Use and Symptom Distress

AIM 2: Patient-Driven Self-Tracking

MODEL: Design Considerations for Tracking Tools

CONTRIBUTION & FUTURE WORK51

DIS

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How do we design tracking tools that are used and

accepted by both patients and clinicians in standard cancer

care?

CO

NC

EPTU

AL M

OD

EL

Why are Tracking Tools Not Actually Used in Standard Cancer Care?

“The approaches that are being used to develop eHealth technologies are not productive enough to create technologies that are meaningful, manageable, and sustainable.”

- Julia van Gemert-Pijnen

University of Twente, Netherlands

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Theories Informing Use and Acceptance of Tracking Tools

• Technology Acceptance Model (TAM)

• Derivations of TAM

• Personal Informatics Stage-Based

Model

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CO

NC

EPTU

AL M

OD

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Technology Acceptance Model (TAM)

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Davis 1989

CO

NC

EPTU

AL M

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Continued Use ModelC

ON

CEPTU

AL M

OD

EL

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Kim & Malhotra 2005

Unified Theory of Acceptance and Use of Technology (UTAUT)

CO

NC

EPTU

AL M

OD

EL

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Venkatesh 2003

Issues with TAM and its Derivations

• Changing facilitators affect continued

use

• Focuses on environment and user

conditions, not technology design

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CO

NC

EPTU

AL M

OD

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Stage-Based Model of Personal Informatics Systems

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CO

NC

EPTU

AL M

OD

EL

Li et al, CHI 2010

Issues with the Stage-based Model

• Missing properties of tracking tools

• No clinician representation

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CO

NC

EPTU

AL M

OD

EL

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CO

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EPTU

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Marypatient withbreast cancer

TRACKING TOOL

Dimensions• Modality• General vs.

Condition-specific• Manual vs.

Automatic• Universal vs.

Personalized• Integration with EHR

TRACKING TOOL

Dimensions• Structure of Data• Clinical Relevance• Completeness• Type of Vocabulary• Actual vs.

Estimated• Timing of Capture• Private vs. Shared

DATA

TRACKING TOOL

Patient Priorities

PATIENTDimensions• Symptom Distress• Behavioral

Intention• Comfort with

Technology

DATA

TRACKING TOOL

Patient Priorities

PATIENTDimensions• Symptom Distress• Behavioral

Intention• Comfort with

Technology

DATA

CLINICIANDimensions• Specialization• Behavioral

Intention• Comfort with

Technology

Clinician Priorities

TRACKING TOOL

Patient Priorities

DATA

Clinician Priorities

PATIENT CLINICIAN

TRACKING TOOL

Patient Priorities

DATA

Clinician Priorities

ACCEPTANCE

PATIENT CLINICIAN

TRACKING TOOL

Patient Priorities

DATA

Clinician Priorities

ACCEPTANCE

ACCEPTANCE

PATIENT CLINICIAN

Self-Tracking in Cancer Care

MOTIVATION: Why Track Symptoms in Cancer

Care?

AIM 1: ePRO Tool Use and Symptom Distress

AIM 2: Patient-Driven Self-Tracking

MODEL: Design Considerations for Tracking Tools

CONTRIBUTION & FUTURE WORK69

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Contribution to Health Informatics

• Uses a larger sample of voluntary ePRO tool use than prior studies

• Supports convergence of multiple types of tracking tools

• Considers how to integrate patient-driven tracking tools into healthcare

• Introduces a new model that has implications for future tracking tool design

70

CO

NTR

IBU

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N

Contribution to Human-Computer Interaction

• Provides tracking tool design considerations for people with serious illnesses like cancer

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CO

NTR

IBU

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N

Future Work

• Validate model

• Interviews with patients and clinicians

• Surveys

• Design new tracking tools for cancer care

72

FUTU

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OR

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Thank You!

CommitteeWanda Pratt, PhDDonna Berry, RN, PhDPaul Gorman, MDTom Payne, MDBeth Devine, PharmD, PhD

Participants in studiesNIH R01 GrantsNLM Informatics Fellowship

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Pedja KlasnjaAndrea HartzlerEun Kyoung ChoeSharbani RoyLauren Wilcox-PattersonLeila ZelnickNadia AkhtarRachel HanischLaurence RohmerSarah MennickenBas de VeerSameer HalaiJared BauerAlan Au

Persona images:Courtesy of Limeade

74

Deepa, Alpa, Payal, NeelamDasha & AlisherMichelleAishaShannonMary CzDUBMSR summer interns ‘12, ‘13FHCRC Communicating for the CureBHI-2008, BHIstudent @ UWTito’s asado crewSoccer friendsHoldem @ homeFellow NLM fellowsWISH colleagues

iMed lab

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Questions? Rupa Patel rupatel@uw.edu

Regina Holliday, Artist & Patient Advocate, Washington, DC

RQ2Is frequent voluntary use of an ePRO tool associated with a reduction in symptom distress of patients with cancer?

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AIM

1: V

OLU

NTA

RY U

SE

“Self-tracking” defined

Awareness of bodily symptoms and their

impact on daily activities and cognitive

processes that is captured either through

measurement or observations and self-report

77

RELA

TED

WO

RK

RQ2

78

AIM

1: e

PR

O T

OO

L USE

Frequent users’ symptom distress was almost significantly higher in voluntary uses between T2 and T3 study time points (p < .07)

TRACKING TOOL

Dimensions• Modality• General vs. Condition-

specific• Manual vs. Automatic• Universal vs. Personalized• Integration with EHR

Dimensions• Structure of Data• Clinical Relevance• Completeness• Type of Vocabulary• Actual vs. Estimated• Timing of Capture• Private vs. Shared

Patient Priorities

DATAClinician Priorities

ACCEPTANCE

ACCEPTANCE

PATIENTDimensions• Symptom Distress• Behavioral Intention• Comfort with

Technology

CLINICIANDimensions• Specialization• Behavioral Intention• Comfort with

Technology