Mina Kwon - 2019.11.12 - Introduction Introduction mHealth , - - PowerPoint PPT Presentation

Mina Kwon

• 2019.11.12 -

Introduction

Introduction

• “mHealth”, mobile health intervention, is a popular option

for losing weight.

• “Dietary lapses”, non-adherence to reduced calorie dietary

prescriptions, is a key challenge for weight management.

• “Simplicity of the mHealth”, giving same intervention to all

users disconnected in time and place from when they are most needed.

Introduction

• Just-in-time adaptive interventions (JITAIs)

: collect and analyze data in real time to deliver tailored interventions during moments of need

• With basic assumptions that,
• A. Lapses are more likely during specific contexts, (such

as watching television, presence of food cues, negative or positive mood, sleep deprivation)

• B. Individual difference exists!

Introduction

• “OnTrack (OT)” , an app-based JITAI and the first JITAI

designed to enhance weight loss.

• 1. using a machine learning algorithm to build

increasingly accurate models of adherence VS lapse behavior,

• 2. predict lapses before they occur
• 3. deliver interventions when calculated lapse risk is

elevated

Primary Aim

Examine the additive efficacy of OnTrack in promoting weight loss compared to previous mHealth intervention. “JITAI vs Non-JITAI”

METHOD

• 181 adults with overweight or obesity, seeking weight loss.
• Weight Watchers (WW): n = 62
• Weight Watchers + OnTrack (WW + OT): n = 119
• Inclusion criteria
• iPhone users
• purchase or lent wireless body scale
• BMI 25 ~ 50 (kg/m2)
• Exclusion criteria
• recent weight loss of 5% or more
• history of bariatric surgery
• pregnant
• eating disorder symptomatology

Participants

Weight Watchers Intervention

• Assign weight watchers points goal designed to achieve

negative energy balance (calorie intake < calorie usage)

• Nudged toward healthier food choice

Beyond the Scale (BTS) Assign smart points goal Freestyle (FS) Same but More zero-point food More Flexible!

OnTrack Intervention

• When users enter new data for lapses risk factors,

machine learning algorithms update associations between reported risk factors and the absence or presence of a lapse.

• Cost-sensitive ensemble models that combined a

weighted vote of predictions from logic boost, bagging, random subspace, random forest, and Bayes net.

• Combination of group-level and individual-level data.

OnTrack Intervention

Group-level algorithm Individual-level algorithm Predicting individual’s lapse with existing model from previous studies based on BTS program. Predicting individual’s lapse with previous data of the same individual

• > Personalize existing group-level algorithm to each individual

8 weeks 2 weeks

No risk alerts Risk alerts ON

OnTrack Intervention

• Alert delivered within several minutes of data update,

when the algorithm calculated an elevated risk of lapse

• 1. Alert
• 2. Short interventions
• 3. Long interventions

OnTrack Intervention

• Alert delivered within several minutes of data update,

when the algorithm calculated an elevated risk of lapse

• 1. Alert
• 2. Short interventions
• 3. Long interventions

Study design

Weight Watchers (WW) WW + OnTrack Beyond the scale (BTS)

18 40

Freestyle (FS)

44 79

Total

62 119

Measures

Weight Watchers (WW) WW + OnTrack

• Weight (weekly self-measured)
• Satisfaction: The Technology Acceptance Model Scales (TAMS)
• Perceived accuracy,

helpfulness of intervention (1 time / day)

• Dietary lapses
• Self report of Lapse triggers

(6 times /day)

• Difference in weight loss between condition (WW / WW + OT) and by

diet type (BTS / FS): 2 x 2 factorial ANCOVA

• Difference in Satisfaction between each diet type (BTS / FS):

independent sample t-test

• Difference in number of lapses across Time and diet type, in OnTrack

group only: Repeated measures ANCOVA

• Difference in Intervention access across Time and diet type, in on

Track group only: Repeated measures ANCOVA

• Accuracy of machine learning models: prediction of the model was

compared with user-reported lapses.

• non-lapse after risk alerts: considered as a true positives

Data analytic strategy

RESULTS

Participant characteristics

RESULTS

Weight Loss Satisfaction

Main effect (p = 0.7) Interaction effect (p = .002) BTS > FS (p = 0.005)

RESULTS

Lapse frequency by week (WW + OT condition) Intervention access (WW + OT condition)

Decreased through time (p < .001) diet type (p = .58) No difference through time (p = .67) BTS > FS (p =.01)

RESULTS

Access to Short intervention: 46.8% (SD = 26.18) Access to Long intervention: 7.4% (SD = 10.10)

• Sensitivity (true positives): 69.2% (SD = 24.87)
• Specificity (true negatives): 83.8% (SD = 10.37)
• Accuracy: BTS > FS

Algorithm Accuracy

DISCUSSION

• More weight loss with JITAI only with the BTS program, on

which the machine language was originally trained. —> Get along with more strict diet type? —> Poor generalization of the OnTrack algorithms!

Discussions

• Intervention access declined over time

—> became familiar with the repeated intervention?

• Little access to long intervention

—> short and quickly digestible intervention is preferred

Discussions

• Weight Watchers (WW-only) condition lacks data of

dietary lapses & engagement.

• Non lapse after alert might be False positive rather than

True positive —> Should be identified!

• Mostly white & Female
• No Follow-up assessment

Limitations

• Replications with a larger RCT
• Generalizable JITAI machine learning algorithm
• Options for increasing engagement
• Solution to reduce burden of entering data

Future directions

Thank you :)