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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Care?





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4

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NMarypatient withbreast cancer

Thumb infection

Insomnia

Pain

Unstable glucose level

Weight loss

Fatigue & Nausea

Hives Dry cough

High blood pressureSwelling

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Symptom Communication

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Nausea

Fatigue

Pain

Weight loss

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{ CLINIC VISIT }

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Nausea

Fatigue

Pain

Weight loss

Swelling Neuropathy


{ CLINIC VISIT }

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Thumb infection

Vaginal dryness

Dry cough

Anxiety

..

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Nausea

Fatigue

Pain

Weight loss

Swelling Neuropathy


{ CLINIC VISIT }

• Care based on continuous healing relationships

• Shared knowledge and the free flow of information

• Personalization based on patient needs and values

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Institute of Medicine (IOM) Report, 2001

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Communication Needs in Cancer Care

Can Tracking Tools Help?

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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

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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

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Cella et al, Journal of Clinical Oncology, 1993

PRO

ESRA-C2

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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

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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

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EMA

Benefits of Ecological Momentary Assessment Tools

• Limited recency effects

• Improved event recall

• Capture of mood and context

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

EMA

• Patient-initiated tracking

• One generates data for personal insight and action

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Personal InformaticsPInf

Benefits of Personal Informatics Self-Tracking

• Clinic adoption not needed

• Wide selection of apps and devices

• Consumer-facing interface design

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PInf

Barriers to Tracking Tool Use

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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

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How do we design tracking tools that are used and

accepted by both patients and clinicians in standard cancer

care?

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Care?





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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?

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AIM

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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+

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AIM

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Berry, ASCO 2012

ESRA-C2

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AIM

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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

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Berry, ASCO 2012

Data Collection Procedures

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AIM

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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

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Overall Use of ESRA-C2

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AIM

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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

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v

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AIM

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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

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L USEDependent Variable

• SDS15 score• Range: 15 - 60

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

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AIM

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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)

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AIM

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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

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L USE

S1 S2 S3 S420

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

Sym

pto

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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

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Aim 1 Summary

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AIM

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• 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

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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

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Data Collection Methods

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

observations• interviews• questionnaires

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AIM

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“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

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HealthWeaver Graphing

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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

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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

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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

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Barrier: Fragmentation of Data

• Paper, MS Office used to track

• Difficult to reflect

• Data unified by just 1 participant

44P9’s notebook

AIM

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HealthWeaver Tracking Usage

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Metrics required for study

Default metrics in HealthWeaver

Average metrics tracked = 8.8(n=10)

AIM

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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

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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.”

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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.”

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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

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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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How do we design tracking tools that are used and

accepted by both patients and clinicians in standard cancer

care?

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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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Technology Acceptance Model (TAM)

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Davis 1989

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Continued Use ModelC

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Kim & Malhotra 2005

Unified Theory of Acceptance and Use of Technology (UTAUT)

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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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Stage-Based Model of Personal Informatics Systems

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EPTU

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Li et al, CHI 2010

Issues with the Stage-based Model

• Missing properties of tracking tools

• No clinician representation

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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


Technology

DATA

CLINICIANDimensions• Specialization• Behavioral


Technology

Clinician Priorities

TRACKING TOOL

Patient Priorities

DATA


PATIENT CLINICIAN

TRACKING TOOL

Patient Priorities

DATA


ACCEPTANCE

PATIENT CLINICIAN

TRACKING TOOL

Patient Priorities

DATA


ACCEPTANCE

ACCEPTANCE

PATIENT CLINICIAN



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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

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Contribution to Human-Computer Interaction

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

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Future Work

• Validate model

• Interviews with patients and clinicians

• Surveys

• Design new tracking tools for cancer care

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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

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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 [email protected]

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

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“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

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RQ2

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AIM

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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

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

Health & Medicine

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