Assessing Moderated Effects of Mobile...

Assessing Moderated Effects

of Mobile Health

Interventions

on Behavior

Susan Murphy

09.28.16

HeartSteps

JITAI

JITAIs

JITAIs

JITAIs

2

The Dream!

“Continually Learning Mobile Health Intervention”

• Help you achieve, and maintain, your desired

long term healthy behaviors

– Provide sufficient short term reinforcement to enhance

your ability to achieve your long term goal

• The ideal mobile health intervention

– will engage you when you need it and will not intrude

when you don’t need it.

– will adjust to unanticipated life events

Example: Heart Steps

oGoal: Develop an mobile activity coach

for individuals who have coronary artery

disease

oThree iterative studies:

o 42 day pilot micro-randomized study with

sedentary individuals,

o 90 day micro-randomized study,

o 365 day personalized study3

Heart Steps

Data from: Wearable band → activity and

sleep quality; Smartphone sensors →

busyness of calendar, location, weather;

Self-report→ stress, utility

a) In which contexts should the smartphone

provide the user with an activity suggestion?

b) On which evenings should the smartphone

ping the user to plan his/her physical activity

over the next day?4

5

Data from wearable devices that

sense and provide treatments

• On each individual: ��, ��, ��, … , �� , �� , ��, …

• t: Decision point

• ��: Observations at tth decision point (high

dimensional)

• ��: Action at tth decision point (treatment)

• ��: Proximal outcome (e.g., reward, utility, cost)

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Examples

1) Decision Points (Times, t, at which a

treatment can be provided.)1) Regular intervals in time (e.g. every 10

minutes)

2) At user demand

Heart Steps: a) approximately every 2-2.5 hours

(activity suggestions) and

b) every evening (planning prompts)

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Examples

2) Observations Ot

1) Passively collected (via sensors)

2) Actively collected (via self-report)

Heart Steps: classifications of activity,

location, weather, step count, busyness of

calendar, usefulness ratings, adherence…….

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Examples

3) Actions At

1) Types of treatments that can be provided at

a decision point, t

2) Whether to provide a treatment

Heart Steps: a) tailored activity suggestion (yes/no)

and b) evening planning prompt (yes/no)

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Tailored Walking

Activity Suggestion

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Examples

4) Proximal Outcome Yt+1

Heart Steps: a) Step count over next 30 minutes

(activity suggestions),

b) Step count on following day (planning prompts)

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Continually Learning Mobile Health

Intervention

1) Trial Designs: Are there effects of the actions on the

proximal response? experimental design

2) Data Analytics for use with trial data: Do effects vary by

the user’s internal/external context,? Are there delayed

effects of the actions? causal inference

3) Learning Algorithms for use with trial data: Construct a

“warm-start” treatment policy. batch Reinforcement

Learning

4) Online Algorithms that personalize and continually update

the mHealth Intervention. online Reinforcement Learning

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Heart Steps Micro-Randomized Trial

For each of n individuals and at each of T decision

points, treatment is repeatedly randomized:

a) Activity suggestion (T=210 randomizations)

• Provide a suggestion with probability .6; do nothing

with probability .4

b) Evening planning prompt (T=42)

• Prompt activity planning for following day with

probability .5; do nothing with probability .5

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Conceptual Models

Generally data analysts fit a series of increasingly more

complex models:

Yt+1 “~” α0 + α1T Zt + β0 At

and then next,

Yt+1 “~” α0 + α1T Zt + β0 At + β1 At St

and so on…

• Yt+1 is subsequent activity over next 30 min.

• At = 1 if activity suggestion and 0 otherwise

• Zt summaries formed from t and past/present observations

• St potential moderator (e.g., current weather is good or not)

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Conceptual Models

Generally data analysts fit a series of increasingly more

complex models:

Yt+1 “~” α0 + α1T Zt + β0 At

and then next,


and so on…

α0 + α1T Zt is used to reduce the noise variance in Yt+1

(Zt is sometimes called a vector of control variables)

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Causal Effects

Yt+1 “~” α0 + α1T Zt + β0 At

β0 is the effect, marginal over all observed and all

unobserved variables, of the activity suggestion on

subsequent activity


β0 + β1 is the effect when the weather is good (St=1),

marginal over other observed and all unobserved variables,

of the activity suggestion on subsequent activity.

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Data Scientist’s Goal

• Challenges:

• Time-varying treatment (At , t=1,…T)

• “independent” variables: Zt, St that may be

affected by prior treatment

• Develop data analytic methods that are

consistent with the scientific understanding of

the meaning of the β regression coefficients

• Robustly facilitate noise reduction via use of

controls, Zt

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“Centered and Weighted

Least Squares Estimation”

• Simple method for complex data!

• Enables unbiased inference for a causal,

marginal, treatment effect (the β‘s)

• Inference for treatment effect is not biased by

how we use the controls to reduce the noise

variance in Yt+1

https://arxiv.org/abs/1601.00237

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Application of the “Centered and

Weighted Least Squares Estimation”

method in an initial analysis of

Heart Steps

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Heart Steps Pilot Study

On each of n=37 participants:

a) Activity suggestion, At

• Provide a suggestion with probability .6

• a tailored sedentary-reducing activity suggestion

(probability=.3)

• a tailored walking activity suggestion (probability=.3)

• Do nothing (probability=.4)

• 5 times per day * 42 days= 210 decision points

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Conceptual Models

Yt+1 “~” α0 + α1Zt + β0 At

Yt+1 “~” α0 + α1Zt + β0 At + β1 At dt

• t=1,…T=210

• Yt+1 = log-transformed step count in the 30 minutes after

to the tth decision point,

• At = 1 if an activity suggestion is delivered at the tth

decision point; At = 0, otherwise,

• Zt = log-transformed step count in the 30 minutes prior to

the tth decision point,

• dt =days in study; takes values in (0,1,….,41)

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Pilot Study Analysis

Yt+1 “~” α0 + α1Zt + β0 At , and

Yt+1 “~” α0 + α1Zt + β0 At + β1 At dt

Causal Effect Term Estimate 95% CI p-value

β0 At

(effect of an activity suggestion)

β��=.13 (-0.01, 0.27) .06

β0 At + β1 At dt

(time trend in effect of an

activity suggestion)

β��

= .51

β��

= -.02

(.20, .81)

(-.03, -.01)

<.01

<.01

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a) Activity suggestion

• Provide a suggestion with probability .6

• a tailored walking activity suggestion

(probability=.3)

• a tailored sedentary-reducing activity suggestion

(probability=.3)

• Do nothing (probability=.4)

• 5 times per day * 42 days= 210 decision points

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Yt+1 “~” α0 + α1Zt + β0 A1t + β1 A2t

• A1t = 1 if walking activity suggestion is delivered at the tth

decision point; A1t = 0, otherwise,

• A2t = 1 if sedentary-reducing activity suggestion is

delivered at the tth decision point; A2t = 0, otherwise,

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Initial Conclusions

• The data indicates that there is a causal

effect of the activity suggestion on step

count in the succeeding 30 minutes.

• This effect is primarily due to the walking

activity suggestions.

• This effect deteriorates with time

• The walking activity suggestion initially increases

step count over succeeding 30 minutes by 271

steps but by day 20 this increase is only 65 steps.

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b) Evening planning prompt, At

• Provide a prompt with probability .5• Prompt using unstructured activity planning for

following day with probability=.25

• Prompt using structured activity planning for


• Do nothing with probability=.5

• 1 time per day * 42 days= 42 decision points

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Conceptual Models

Yt+1 “~” α0 + α1Zt + β0 At

Yt+1 “~” α0 + α1Zt + β0 At Wt + β1 At (1-Wt)

• t=1,…T=42

• Yt+1 = square root-transformed step count on the day after

the tth day,

• At = 1 if activity planning prompt on the evening of the tth

day; At = 0, otherwise,

• Zt = square-root step count on the tth day,

• Wt =1 if Sunday through Thursday; Wt =0, otherwise

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Yt+1 “~” α0 + α1Zt + β0 At , and

Yt+1 “~” α0 + α1Zt + β0 At Wt + β1 At (1-Wt)


β0 At

(effect of planning)

β��= 1.7 ns ns

β0 At Wt + β1 At (1-Wt)

(effect of planning for

weekday (Wt =1) and

for weekend(Wt =0)

β��

= 3.6

β��

<0

(.74, 6.4)

ns

<.02

ns

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Heart Steps Pilot Data


b) Evening planning prompt

• Provide a prompt with probability .5• Prompt using unstructured activity planning for


• Prompt using structured activity planning for


• Do nothing with probability=.5

• 1 time per day * 42 days= 42 decision points

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Conceptual Model

Yt+1 “~” α0 + α1Zt + β0 A1t + β1 A2t

• Yt+1 = square root-transformed step count on the day after

the tth day,

• A1t = 1 if unstructured activity planning prompt on the

evening of the tth day; A1t = 0, otherwise,

• A2t = 1 if structured activity planning prompt on the


• Zt = square-root step count on the tth day,

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Yt+1 “~” α0 + α1Zt + β0 A1t + β1 A2t t=0,…T=41

• A1t = 1 if unstructured activity planning prompt on the


• A2t = 1 if structured activity planning prompt on the



β0 A1t + β1 A2t β��

= 3.1

β��

>0

(-.22, 6.4)

ns

.07

ns

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Heart Steps Pilot Study Analysis

Yt+1 “~” α0 + α1Zt + β0 A1t Wt + β1 A1t (1-Wt)

+ β2 A2t Wt + β3 A2t (1-Wt)

A1t = 1 if unstructured activity planning prompt on the


Wt =1 if Sunday through Thursday; Wt =0, otherwise

Causal Effect Estimate 95% CI p-value

β0 A1t Wt + β1 A1t (1-Wt)

+ β2 A2t Wt + β3 A2t (1-Wt)

β��

= 5.3

β��

<0, β��

>0,

β�

<0

(2.2, 8.5)

all ns

<.01

all ns

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Initial Conclusions

• The data indicates that there is a causal

effect of planning the next day’s activity on

on the following day’s step count

• This effect is due to the unstructured planning

prompts.

• This effect occurs primarily on weekdays

• On weekdays the effect of an unstructured

planning prompt is to increase step count on the

following day by approximately 780 steps.

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Discussion

Problematic Analyses

• GLM & GEE analyses

• Random effects models & analyses

• Machine Learning Generalizations:

– Partially linear, single index models & analysis

– Varying coefficient models & analysis

--These analyses do not take advantage of the micro-

randomization. Can accidentally eliminate the advantages of

randomization for estimating causal effects--

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Discussion

• Randomization enhances:

– Causal inference based on minimal structural

assumptions

• Challenge:

– How to include random effects which reflect

scientific understanding (“person-specific” effects)

yet not destroy causal inference?

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It takes a Team!

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