A Methodologic Systematic Review of Mobile Health Behavior Change Randomized Trials

Oikonomidi, Theodora; Vivot, Alexandre; Tran, Viet-Thi; Robin, Elisabeth; Ravaud, Philippe

doi:10.1016/j.amepre.2019.07.008

Cited by 24 publications

(7 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Following a systematic review, Badawy et al [ 31 ] concluded that mobile phone app interventions could improve medication adherence among adolescents with chronic health conditions, and the current literature indicates that these mobile phone app interventions are feasible and accepted by adolescents, and there is modest evidence to support the efficacy of these interventions. These findings are consistent with those of Oikonomidi et al [ 32 ], who conducted a systematic review of mHealth behavior change interventions (SMS text messages and smartphone apps) in RCTs. After reviewing 231 RCTs, the authors concluded that mHealth behavior change interventions lack information that would be useful for providers, including the long-term impact of the interventions’ health outcomes and information needed for replication of the RTC.…”

Section: Introductionsupporting

confidence: 92%

Effect of a Mobile Health App on Adherence to Physical Health Treatment: Retrospective Analysis

Greenstein

Topp

Etnoyer-Slaski

et al. 2021

JMIR Rehabil Assist Technol

View full text Add to dashboard Cite

Background Adherence to prescribed medical interventions can predict the efficacy of the treatment. In physical health clinics, not adhering to prescribed therapy can take the form of not attending a scheduled clinic visit (no-show appointment) or prematurely terminating treatment against the advice of the provider (self-discharge). A variety of interventions, including mobile phone apps, have been introduced for patients to increase their adherence to attending scheduled clinic visits. Limited research has examined the impact of a mobile phone app among patients attending chiropractic and rehabilitation clinic visits. Objective This study aims to compare adherence to prescribed physical health treatment among patients attending a chiropractic and rehabilitation clinic who did and did not choose to adopt a phone-based app to complement their treatment. Methods The medical records of new patients who presented for care during 2019 and 2020 at 5 community-based chiropractic and rehabilitation clinics were reviewed for the number of kept and no-show appointments and to determine whether the patient was provider-discharged or self-discharged. During this 24-month study, 36.28% (1497/4126) of patients seen in the targeted clinics had downloaded the Kanvas app on their mobile phone, whereas the remaining patients chose not to download the app (usual care group). The gamification component of the Kanvas app provided the patient with a point every time they attended their visits, which could be redeemed as an incentive. Results During both 2019 and 2020, the Kanvas app group was provider-discharged at a greater rate than the usual care group. The Kanvas app group kept a similar number of appointments compared with the usual care group in 2019 but kept significantly more appointments than the usual care group in 2020. During 2019, both groups exhibited a similar number of no-show appointments; however, in 2020, the Kanvas app group demonstrated more no-show appointments than the usual care group. When collapsed across years and self-discharged, the Kanvas app group had a greater number of kept appointments compared with the usual care group. When provider-discharged, both groups exhibited a similar number of kept appointments. The Kanvas app group and the usual care group were similar in the number of no-show appointments when provider-discharged, and when self-discharged, the Kanvas app group had more no-show appointments compared with the usual care group. Conclusions Patients who did or did not have access to the Kanvas app and were provider-discharged exhibited a similar number of kept appointments and no-show appointments. When patients were self-discharged and received the Kanvas app, they exhibited 3.2 more kept appointments and 0.94 more no-show appointments than the self-discharged usual care group.

show abstract

Section: Introductionsupporting

confidence: 92%

Effect of a Mobile Health App on Adherence to Physical Health Treatment: Retrospective Analysis

Greenstein

Topp

Etnoyer-Slaski

et al. 2021

JMIR Rehabil Assist Technol

View full text Add to dashboard Cite

show abstract

“…We have conducted a preliminary search for existing systematic reviews and review protocols on mHealth-supported behavior change interventions. We identified several reviews that aimed to evaluate the efficacy of mHealth behavior change interventions in a range of populations [46][47][48], evaluate methodologies for assessing mHealth behavior change randomized trials [49], and assess the diversity of behavior change techniques and theories in mHealth interventions [35,48]. However, syntheses of features unique to mHealth intervention design, including personalization, real-time functions, and deliverable resources, are lacking in the literature.…”

Section: Discussionmentioning

confidence: 99%

Design Principles in mHealth Interventions for Sustainable Health Behavior Changes: Protocol for a Systematic Review

Yang¹,

Kuang²,

Xu³

et al. 2023

JMIR Res Protoc

View full text Add to dashboard Cite

Background In recent years, mHealth has increasingly been used to deliver behavioral interventions for disease prevention and self-management. Computing power in mHealth tools can provide unique functions beyond conventional interventions in provisioning personalized behavior change recommendations and delivering them in real time, supported by dialogue systems. However, design principles to incorporate these features in mHealth interventions have not been systematically evaluated. Objective The goal of this review is to identify best practices for the design of mHealth interventions targeting diet, physical activity, and sedentary behavior. We aim to identify and summarize the design characteristics of current mHealth tools with a focus on the following features: (1) personalization, (2) real-time functions, and (3) deliverable resources. Methods We will conduct a systematic search of electronic databases, including MEDLINE, CINAHL, Embase, PsycINFO, and Web of Science for studies published since 2010. First, we will use keywords that combine mHealth, interventions, chronic disease prevention, and self-management. Second, we will use keywords that cover diet, physical activity, and sedentary behavior. Literature found in the first and second steps will be combined. Finally, we will use keywords for personalization and real-time functions to limit the results to interventions that have reported these design features. We expect to perform narrative syntheses for each of the 3 target design features. Study quality will be evaluated using the Risk of Bias 2 assessment tool. Results We have conducted a preliminary search of existing systematic reviews and review protocols on mHealth-supported behavior change interventions. We have identified several reviews that aimed to evaluate the efficacy of mHealth behavior change interventions in a range of populations, evaluate methodologies for assessing mHealth behavior change randomized trials, and assess the diversity of behavior change techniques and theories in mHealth interventions. However, syntheses on the unique features of mHealth intervention design are absent in the literature. Conclusions Our findings will provide a basis for developing best practices for designing mHealth tools for sustainable behavior change. Trial Registration PROSPERO CRD42021261078; https://tinyurl.com/m454r65t International Registered Report Identifier (IRRID) PRR1-10.2196/39093

show abstract

“…Rapid developments in technology have allowed wearable devices to measure a plethora of health-related quantities such as gait, body temperature, and blood oxygen saturation [36]. Mobile phones are increasingly being used for behaviour change interventions; see, for example, a review by Oikonomidi et al (2019) [37]. The problem of missing data will pertain to this vast range of outcomes in wearables.…”

Section: Discussionmentioning

confidence: 99%

A framework for handling missing accelerometer outcome data in trials

Tackney

Cook

Ståhl

et al. 2021

Trials

View full text Add to dashboard Cite

Accelerometers and other wearable devices are increasingly being used in clinical trials to provide an objective measure of the impact of an intervention on physical activity. Missing data are ubiquitous in this setting, typically for one of two reasons: patients may not wear the device as per protocol, and/or the device may fail to collect data (e.g. flat battery, water damage). However, it is not always possible to distinguish whether the participant stopped wearing the device, or if the participant is wearing the device but staying still. Further, a lack of consensus in the literature on how to aggregate the data before analysis (hourly, daily, weekly) leads to a lack of consensus in how to define a “missing” outcome. Different trials have adopted different definitions (ranging from having insufficient step counts in a day, through to missing a certain number of days in a week). We propose an analysis framework that uses wear time to define missingness on the epoch and day level, and propose a multiple imputation approach, at the day level, which treats partially observed daily step counts as right censored. This flexible approach allows the inclusion of auxiliary variables, and is consistent with almost all the primary analysis models described in the literature, and readily allows sensitivity analysis (to the missing at random assumption) to be performed. Having presented our framework, we illustrate its application to the analysis of the 2019 MOVE-IT trial of motivational interviewing to increase exercise.

show abstract

A Methodologic Systematic Review of Mobile Health Behavior Change Randomized Trials

Cited by 24 publications

References 38 publications

Effect of a Mobile Health App on Adherence to Physical Health Treatment: Retrospective Analysis

Effect of a Mobile Health App on Adherence to Physical Health Treatment: Retrospective Analysis

Design Principles in mHealth Interventions for Sustainable Health Behavior Changes: Protocol for a Systematic Review

A framework for handling missing accelerometer outcome data in trials

Contact Info

Product

Resources

About