Tracking the temporal flows of mobile communication in daily life

Ross, Morgan Quinn; Bayer, Joseph B.; Rhee, Lisa; Potti, Ivory; Chang, Yi‐Jun

doi:10.1177/14614448231158646

Cited by 2 publications

(1 citation statement)

References 61 publications

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“…The "anytime-anyplace" mobile access enables wide reach and improves how we coordinate and communicate [7][8][9][10] . In addition, mobile log data help identify evidence-based strategies for health intervention and message delivery [11][12][13][14][15] . Researchers have conducted studies that differentiate message expression from message reception across various communication levels, and found composing one-on-one messages is associated with greater perceived bonding and drafting public forum posts is linked to decreased risky drinking behaviors 16 .…”

Section: Introductionmentioning

confidence: 99%

mHealth Engagement for Antiretroviral Medication Adherence Among People With HIV and Substance Use Disorders: Observational Study (Preprint)

Mi,

Yang,

Tahk

et al. 2024

Preprint

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BACKGROUND Despite the increasing popularity of mobile health (mHealth) technologies, little is known about which types of mHealth system engagement might affect the maintenance of antiretroviral therapy (ART) among people with HIV (PWH) and substance use disorders (SUD). OBJECTIVE Using longitudinal and detailed system logs and weekly survey data, we tested a mediation model, where mHealth engagement indicators were treated as predictors, substance use and confidence in HIV management as joint mediators, and ART adherence as the outcome. We further distinguished the initiation and intensity of system engagement by mode (expression vs. reception) and by communication levels (intraindividual vs. dyadic vs. network). METHODS Tailored for PWH living with SUD, the mHealth app was distributed among 208 participants aged over 18 from two U.S. health clinics. Supervised by medical professionals, participants received weekly surveys through the app to report their health status and medication adherence data. System use was passively collected through the app, operationalized as transformed click-level data, aggregated weekly, and connected to survey responses with a 7-day lagged window. Linear regression and structure equation models (SEMs) with cluster-robust standard errors were employed for analyses, controlling within-person autocorrelation and group-level error correlations. Racial groups were examined as moderators in the SEMs to address specific research questions. RESULTS We found that 1) intensity, not initiation, of system use, 2) dyadic message expression and reception, and 3) network expression, positively predicted medication adherence via joint mediators (substance use and confidence in HIV management). However, intraindividual reception (i.e., re-reading saved entries for personal motivation) negatively predicts medication adherence via joint mediators. We also found Black participants have distinct usage patterns, suggesting the need to tailor mHealth interventions for this subgroup. CONCLUSIONS These findings highlight the importance of considering the intensity of system engagement, rather than initiation alone, when designing mHealth interventions for PWH and tailoring these systems to Black communities.

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Section: Introductionmentioning

confidence: 99%

mHealth Engagement for Antiretroviral Medication Adherence Among People With HIV and Substance Use Disorders: Observational Study (Preprint)

Mi,

Yang,

Tahk

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

mHealth Engagement for Antiretroviral Medication Adherence Among People With HIV and Substance Use Disorders: Observational Study

Mi,

Yang,

Tahk

et al. 2024

J Med Internet Res

View full text Add to dashboard Cite

Background Despite the increasing popularity of mobile health (mHealth) technologies, little is known about which types of mHealth system engagement might affect the maintenance of antiretroviral therapy among people with HIV and substance use disorders. Objective This study aimed to use longitudinal and detailed system logs and weekly survey data to test a mediation model, where mHealth engagement indicators were treated as predictors, substance use and confidence in HIV management were treated as joint mediators, and antiretroviral therapy adherence was treated as the outcome. We further distinguished the initiation and intensity of system engagement by mode (expression vs reception) and by communication levels (intraindividual vs dyadic vs network). Methods Tailored for people with HIV living with substance use disorders, the mHealth app was distributed among 208 participants aged >18 years from 2 US health clinics. Supervised by medical professionals, participants received weekly surveys through the app to report their health status and medication adherence data. System use was passively collected through the app, operationalized as transformed click-level data, aggregated weekly, and connected to survey responses with a 7-day lagged window. Using the weekly check-in record provided by participants as the unit of analysis (N=681), linear regression and structure equation models with cluster-robust SEs were used for analyses, controlling within-person autocorrelation and group-level error correlations. Racial groups were examined as moderators in the structure equation models. Results We found that (1) intensity, not initiation, of system use; (2) dyadic message expression and reception; and (3) network expression positively predicted medication adherence through joint mediators (substance use and confidence in HIV management). However, intraindividual reception (ie, rereading saved entries for personal motivation) negatively predicts medication adherence through joint mediators. We also found Black participants have distinct usage patterns, suggesting the need to tailor mHealth interventions for this subgroup. Conclusions These findings highlight the importance of considering the intensity of system engagement, rather than initiation alone, when designing mHealth interventions for people with HIV and tailoring these systems to Black communities.

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Tracking the temporal flows of mobile communication in daily life

Cited by 2 publications

References 61 publications

mHealth Engagement for Antiretroviral Medication Adherence Among People With HIV and Substance Use Disorders: Observational Study (Preprint)

mHealth Engagement for Antiretroviral Medication Adherence Among People With HIV and Substance Use Disorders: Observational Study (Preprint)

mHealth Engagement for Antiretroviral Medication Adherence Among People With HIV and Substance Use Disorders: Observational Study

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