Daily Consistency Over Timing: Routine Formation and Population-Specific Opportunities in mHealth User Adherence

Samia Shahnawaz; Rhyann Clarke; Kyle Landell; M. S. Jovita Rodrigues; Matteo Danieletto; Suzanne Bakken; Noemie Elhadad; Ipek Ensari

doi:10.1145/3772363.3799243

Daily Consistency Over Timing: Routine Formation and Population-Specific Opportunities in mHealth User Adherence

Samia Shahnawaz
, Rhyann Clarke
, Kyle Landell
, M. S. Jovita Rodrigues
, Matteo Danieletto
, Suzanne Bakken
, Noemie Elhadad
, Ipek Ensari

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Understanding mobile Health (mHealth) user engagement patterns and its association to adherence in chronic disease populations is critical for data quality and clinical outcomes. Yet, this remains unexplored and a missed opportunity, given the unique value of mHealth data for traditionally under-documented conditions. Methods: We analyzed 13,997 total days of data from 131 female participants with chronic pelvic pain disorders (CPPDs) and 72 healthy controls. Participants tracked daily symptoms using a research App for 90 days. Using mixed-effects regression, we investigated predictors of adherence and consistency in App engagement, with moderators of CPPD burden and temporal accessibility. Results: Later-day engagement was the strongest predictor of user consistency, independent of pain interference or CPPD. Habitual evening tracking was associated with better adherence for those with CPPD vs healthy controls. Consistency was the strongest predictor of adherence. Conclusion: Prioritizing habit and preference over fixed-time assessments could improve temporal accessibility and data completeness.

Original language	English
Title of host publication	CHI 2026 - Extended Abtracts of the 2026 CHI Conference on Human Factors in Computing Systems
Editors	Nuria Oliver, David A. Shamma, Heloisa Candello, Pablo Cesar, Pedro Lopes, Valentino Artizzu, Fiona Draxler, Gustavo Lopez, Anke V. Reinschluessel, Xin Tong, Phoebe O. Toups Dugas
Publisher	Association for Computing Machinery
ISBN (Electronic)	9798400722813
DOIs	https://doi.org/10.1145/3772363.3799243
State	Published - 13 Apr 2026
Event	Extended Abtracts of the 2026 CHI Conference on Human Factors in Computing Systems, CHI 2026 - Barcelona, Spain Duration: 13 Apr 2026 → 17 Apr 2026

Publication series

Name	Conference on Human Factors in Computing Systems - Proceedings

Conference

Conference	Extended Abtracts of the 2026 CHI Conference on Human Factors in Computing Systems, CHI 2026
Country/Territory	Spain
City	Barcelona
Period	13/04/26 → 17/04/26

Keywords

chronic pain
digital health
engagement
mHealth
temporal adherence

Access to Document

10.1145/3772363.3799243

Cite this

Shahnawaz, S., Clarke, R., Landell, K., Jovita Rodrigues, M. S., Danieletto, M., Bakken, S., Elhadad, N., & Ensari, I. (2026). Daily Consistency Over Timing: Routine Formation and Population-Specific Opportunities in mHealth User Adherence. In N. Oliver, D. A. Shamma, H. Candello, P. Cesar, P. Lopes, V. Artizzu, F. Draxler, G. Lopez, A. V. Reinschluessel, X. Tong, & P. O. Toups Dugas (Eds.), CHI 2026 - Extended Abtracts of the 2026 CHI Conference on Human Factors in Computing Systems Article 197 (Conference on Human Factors in Computing Systems - Proceedings ). Association for Computing Machinery. https://doi.org/10.1145/3772363.3799243

Shahnawaz, Samia ; Clarke, Rhyann ; Landell, Kyle et al. / Daily Consistency Over Timing : Routine Formation and Population-Specific Opportunities in mHealth User Adherence. CHI 2026 - Extended Abtracts of the 2026 CHI Conference on Human Factors in Computing Systems. editor / Nuria Oliver ; David A. Shamma ; Heloisa Candello ; Pablo Cesar ; Pedro Lopes ; Valentino Artizzu ; Fiona Draxler ; Gustavo Lopez ; Anke V. Reinschluessel ; Xin Tong ; Phoebe O. Toups Dugas. Association for Computing Machinery, 2026. (Conference on Human Factors in Computing Systems - Proceedings ).

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title = "Daily Consistency Over Timing: Routine Formation and Population-Specific Opportunities in mHealth User Adherence",

abstract = "Understanding mobile Health (mHealth) user engagement patterns and its association to adherence in chronic disease populations is critical for data quality and clinical outcomes. Yet, this remains unexplored and a missed opportunity, given the unique value of mHealth data for traditionally under-documented conditions. Methods: We analyzed 13,997 total days of data from 131 female participants with chronic pelvic pain disorders (CPPDs) and 72 healthy controls. Participants tracked daily symptoms using a research App for 90 days. Using mixed-effects regression, we investigated predictors of adherence and consistency in App engagement, with moderators of CPPD burden and temporal accessibility. Results: Later-day engagement was the strongest predictor of user consistency, independent of pain interference or CPPD. Habitual evening tracking was associated with better adherence for those with CPPD vs healthy controls. Consistency was the strongest predictor of adherence. Conclusion: Prioritizing habit and preference over fixed-time assessments could improve temporal accessibility and data completeness.",

keywords = "chronic pain, digital health, engagement, mHealth, temporal adherence",

author = "Samia Shahnawaz and Rhyann Clarke and Kyle Landell and \{Jovita Rodrigues\}, \{M. S.\} and Matteo Danieletto and Suzanne Bakken and Noemie Elhadad and Ipek Ensari",

note = "Publisher Copyright: {\textcopyright} 2026 Copyright held by the owner/author(s).; Extended Abtracts of the 2026 CHI Conference on Human Factors in Computing Systems, CHI 2026 ; Conference date: 13-04-2026 Through 17-04-2026",

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month = apr,

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doi = "10.1145/3772363.3799243",

language = "English",

series = "Conference on Human Factors in Computing Systems - Proceedings ",

publisher = "Association for Computing Machinery",

editor = "Nuria Oliver and Shamma, \{David A.\} and Heloisa Candello and Pablo Cesar and Pedro Lopes and Valentino Artizzu and Fiona Draxler and Gustavo Lopez and Reinschluessel, \{Anke V.\} and Xin Tong and \{Toups Dugas\}, \{Phoebe O.\}",

booktitle = "CHI 2026 - Extended Abtracts of the 2026 CHI Conference on Human Factors in Computing Systems",

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Shahnawaz, S, Clarke, R, Landell, K, Jovita Rodrigues, MS, Danieletto, M, Bakken, S, Elhadad, N & Ensari, I 2026, Daily Consistency Over Timing: Routine Formation and Population-Specific Opportunities in mHealth User Adherence. in N Oliver, DA Shamma, H Candello, P Cesar, P Lopes, V Artizzu, F Draxler, G Lopez, AV Reinschluessel, X Tong & PO Toups Dugas (eds), CHI 2026 - Extended Abtracts of the 2026 CHI Conference on Human Factors in Computing Systems., 197, Conference on Human Factors in Computing Systems - Proceedings , Association for Computing Machinery, Extended Abtracts of the 2026 CHI Conference on Human Factors in Computing Systems, CHI 2026, Barcelona, Spain, 13/04/26. https://doi.org/10.1145/3772363.3799243

Daily Consistency Over Timing: Routine Formation and Population-Specific Opportunities in mHealth User Adherence. / Shahnawaz, Samia; Clarke, Rhyann; Landell, Kyle et al.
CHI 2026 - Extended Abtracts of the 2026 CHI Conference on Human Factors in Computing Systems. ed. / Nuria Oliver; David A. Shamma; Heloisa Candello; Pablo Cesar; Pedro Lopes; Valentino Artizzu; Fiona Draxler; Gustavo Lopez; Anke V. Reinschluessel; Xin Tong; Phoebe O. Toups Dugas. Association for Computing Machinery, 2026. 197 (Conference on Human Factors in Computing Systems - Proceedings ).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Daily Consistency Over Timing

T2 - Extended Abtracts of the 2026 CHI Conference on Human Factors in Computing Systems, CHI 2026

AU - Shahnawaz, Samia

AU - Clarke, Rhyann

AU - Landell, Kyle

AU - Jovita Rodrigues, M. S.

AU - Danieletto, Matteo

AU - Bakken, Suzanne

AU - Elhadad, Noemie

AU - Ensari, Ipek

PY - 2026/4/13

Y1 - 2026/4/13

N2 - Understanding mobile Health (mHealth) user engagement patterns and its association to adherence in chronic disease populations is critical for data quality and clinical outcomes. Yet, this remains unexplored and a missed opportunity, given the unique value of mHealth data for traditionally under-documented conditions. Methods: We analyzed 13,997 total days of data from 131 female participants with chronic pelvic pain disorders (CPPDs) and 72 healthy controls. Participants tracked daily symptoms using a research App for 90 days. Using mixed-effects regression, we investigated predictors of adherence and consistency in App engagement, with moderators of CPPD burden and temporal accessibility. Results: Later-day engagement was the strongest predictor of user consistency, independent of pain interference or CPPD. Habitual evening tracking was associated with better adherence for those with CPPD vs healthy controls. Consistency was the strongest predictor of adherence. Conclusion: Prioritizing habit and preference over fixed-time assessments could improve temporal accessibility and data completeness.

AB - Understanding mobile Health (mHealth) user engagement patterns and its association to adherence in chronic disease populations is critical for data quality and clinical outcomes. Yet, this remains unexplored and a missed opportunity, given the unique value of mHealth data for traditionally under-documented conditions. Methods: We analyzed 13,997 total days of data from 131 female participants with chronic pelvic pain disorders (CPPDs) and 72 healthy controls. Participants tracked daily symptoms using a research App for 90 days. Using mixed-effects regression, we investigated predictors of adherence and consistency in App engagement, with moderators of CPPD burden and temporal accessibility. Results: Later-day engagement was the strongest predictor of user consistency, independent of pain interference or CPPD. Habitual evening tracking was associated with better adherence for those with CPPD vs healthy controls. Consistency was the strongest predictor of adherence. Conclusion: Prioritizing habit and preference over fixed-time assessments could improve temporal accessibility and data completeness.

KW - chronic pain

KW - digital health

KW - engagement

KW - mHealth

KW - temporal adherence

UR - https://www.scopus.com/pages/publications/105038071750

U2 - 10.1145/3772363.3799243

DO - 10.1145/3772363.3799243

M3 - Conference contribution

AN - SCOPUS:105038071750

T3 - Conference on Human Factors in Computing Systems - Proceedings

BT - CHI 2026 - Extended Abtracts of the 2026 CHI Conference on Human Factors in Computing Systems

A2 - Oliver, Nuria

A2 - Shamma, David A.

A2 - Candello, Heloisa

A2 - Cesar, Pablo

A2 - Lopes, Pedro

A2 - Artizzu, Valentino

A2 - Draxler, Fiona

A2 - Lopez, Gustavo

A2 - Reinschluessel, Anke V.

A2 - Tong, Xin

A2 - Toups Dugas, Phoebe O.

PB - Association for Computing Machinery

Y2 - 13 April 2026 through 17 April 2026

ER -

Shahnawaz S, Clarke R, Landell K, Jovita Rodrigues MS, Danieletto M, Bakken S et al. Daily Consistency Over Timing: Routine Formation and Population-Specific Opportunities in mHealth User Adherence. In Oliver N, Shamma DA, Candello H, Cesar P, Lopes P, Artizzu V, Draxler F, Lopez G, Reinschluessel AV, Tong X, Toups Dugas PO, editors, CHI 2026 - Extended Abtracts of the 2026 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery. 2026. 197. (Conference on Human Factors in Computing Systems - Proceedings ). doi: 10.1145/3772363.3799243

Daily Consistency Over Timing: Routine Formation and Population-Specific Opportunities in mHealth User Adherence

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