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A Novel Just-in-Time Intervention for Promoting Safer Drinking Among College Students: App Testing Across 2 Independent Pre-Post Trials

A Novel Just-in-Time Intervention for Promoting Safer Drinking Among College Students: App Testing Across 2 Independent Pre-Post Trials

Sample items include “I think that I would like to use bhoos frequently” and “I thought bhoos was easy to use.” Responses to each item range from 1 (strongly disagree) to 5 (strongly agree). Possible scores on the SUS range from 0 to 100, with a higher score indicating higher overall usability of a system or program. The SUS has been used in roughly 3500 surveys within 273 studies evaluating a range of systems, interfaces, and programs [37]. Internal consistency of the SUS was good (α=0.84).

Philip I Chow, Jessica Smith, Ravjot Saini, Christina Frederick, Connie Clark, Maxwell Ritterband, Jennifer P Halbert, Kathryn Cheney, Katharine E Daniel, Karen S Ingersoll

JMIR Hum Factors 2025;12:e69873

Understanding the Relationship Between Mood Symptoms and Mobile App Engagement Among Patients With Breast Cancer Using Machine Learning: Case Study

Understanding the Relationship Between Mood Symptoms and Mobile App Engagement Among Patients With Breast Cancer Using Machine Learning: Case Study

For variables that follow a linear pattern, interpolation can be used to impute missing values between 2 time points; that is, yi = (yi-1 + yi+1)/2, where the value is missing at position i. Alternatively, for variables with unknown or nonlinear patterns of change, more sophisticated methods such as multiple imputations using linear regression can be used [28].

Anna N Baglione, Lihua Cai, Aram Bahrini, Isabella Posey, Mehdi Boukhechba, Philip I Chow

JMIR Med Inform 2022;10(6):e30712

Developing a Process for the Analysis of User Journeys and the Prediction of Dropout in Digital Health Interventions: Machine Learning Approach

Developing a Process for the Analysis of User Journeys and the Prediction of Dropout in Digital Health Interventions: Machine Learning Approach

SHUTi is based on the primary principles of face-to-face cognitive behavioral therapy for insomnia (CBT-I), including sleep restriction, stimulus control, cognitive restructuring, sleep hygiene, and relapse prevention. SHUTi contains 7 cores that are dispensed over time, the first core being a tutorial on how to use the program, with new cores becoming available 7 days after completion of a previous core. This format was meant to mirror traditional CBT-I delivery procedures using a weekly session format.

Vincent Bremer, Philip I Chow, Burkhardt Funk, Frances P Thorndike, Lee M Ritterband

J Med Internet Res 2020;22(10):e17738

Use of Mental Health Apps by Patients With Breast Cancer in the United States: Pilot Pre-Post Study

Use of Mental Health Apps by Patients With Breast Cancer in the United States: Pilot Pre-Post Study

Participants are asked to report (1=never and 5=always) the degree to which they experienced various depressed states (eg, “I felt worthless” and “I felt hopeless”) over the past 7 days. Continuous anxiety symptoms were assessed with the 4-item scale from the PROMIS-29 Profile v2.0. Participants are asked to report (1=never and 5=always) the degree to which they have experienced different anxious states (eg, “My worries overwhelmed me” and “I felt fearful”) over the past 7 days.

Philip I I. Chow, Shayna L Showalter, Matthew Gerber, Erin M Kennedy, David Brenin, David C Mohr, Emily G Lattie, Alisha Gupta, Gabrielle Ocker, Wendy F Cohn

JMIR Cancer 2020;6(1):e16476

A Novel Mobile Phone App Intervention With Phone Coaching to Reduce Symptoms of Depression in Survivors of Women’s Cancer: Pre-Post Pilot Study

A Novel Mobile Phone App Intervention With Phone Coaching to Reduce Symptoms of Depression in Survivors of Women’s Cancer: Pre-Post Pilot Study

Participants respond (0=rarely or none of the time and 3=all of the time) to 10 items that assess symptoms related to depression (eg, I felt lonely and I felt depressed). Participants are asked to base their responses on how they have felt over the past week. A cutoff score of 10 or greater, from a possible score range of 0 to 30, has been used to indicate a clinically significant level of depression in older adults [72].

Philip I I. Chow, Fabrizio Drago, Erin M Kennedy, Wendy F Cohn

JMIR Cancer 2020;6(1):e15750

Use of Mental Health Apps by Breast Cancer Patients and Their Caregivers in the United States: Protocol for a Pilot Pre-Post Study

Use of Mental Health Apps by Breast Cancer Patients and Their Caregivers in the United States: Protocol for a Pilot Pre-Post Study

Participants are asked to report (1=never and 5=always) the degree to which they experienced various depressed states (eg, “I felt worthless” and “I felt hopeless”) over the past 7 days. Continuous anxiety symptoms will be assessed with the 4-item scale from the PROMIS-29 Profile v2.0. Participants are asked to report (1=never and 5=always) how much they have experienced different anxious states (eg, “My worries overwhelmed me” and “I felt fearful”) over the past 7 days.

Philip I Chow, Shayna L Showalter, Matthew S Gerber, Erin Kennedy, David R Brenin, Anneke T Schroen, David C Mohr, Emily G Lattie, Wendy F Cohn

JMIR Res Protoc 2019;8(1):e11452

Informal Caregivers’ Use of Internet-Based Health Resources: An Analysis of the Health Information National Trends Survey

Informal Caregivers’ Use of Internet-Based Health Resources: An Analysis of the Health Information National Trends Survey

In addition, the jackknife variance estimation with repeated replications was used to estimate SEs, which reduces bias and, therefore, type I error. These procedures are in accordance with published HINTS analysis recommendations [37]. Furthermore, alpha of .05 was used to determine significance for all tests.

Kelly M. M. Shaffer, Philip I. Chow, Wendy F. Cohn, Karen S. Ingersoll, Lee M. Ritterband

JMIR Aging 2018;1(2):e11051

Predicting Social Anxiety From Global Positioning System Traces of College Students: Feasibility Study

Predicting Social Anxiety From Global Positioning System Traces of College Students: Feasibility Study

Location entropy: This metric was calculated using the entropy of Shannon [26] to measure how each participant’s time was distributed over different location classes, where pi is the percentage of time spent at location i and n is the total number of visited locations: Transition Frequency from one type of location to another: Given a specific participant and two types of locations (eg, “Home” and “Work”), this metric characterized the frequency at which the participant transited from one type of location to

Mehdi Boukhechba, Philip Chow, Karl Fua, Bethany A Teachman, Laura E Barnes

JMIR Ment Health 2018;5(3):e10101