BackgroundMobile device-based ecological momentary assessment (mobile-EMA) is increasingly used to collect participants' data in real-time and in context. Although EMA offers methodological advantages, these advantages can be diminished by participant noncompliance. However, evidence on how well participants comply with mobile-EMA protocols and how study design factors associated with participant compliance is limited, especially in the youth literature.ObjectiveTo systematically and meta-analytically examine youth’s compliance to mobile-EMA protocols and moderators of participant compliance in clinical and nonclinical settings.MethodsStudies using mobile devices to collect EMA data among youth (age ≤18 years old) were identified. A systematic review was conducted to describe the characteristics of mobile-EMA protocols and author-reported factors associated with compliance. Random effects meta-analyses were conducted to estimate the overall compliance across studies and to explore factors associated with differences in youths’ compliance.ResultsThis review included 42 unique studies that assessed behaviors, subjective experiences, and contextual information. Mobile phones were used as the primary mode of EMA data collection in 48% (20/42) of the reviewed studies. In total, 12% (5/42) of the studies used wearable devices in addition to the EMA data collection platforms. About half of the studies (62%, 24/42) recruited youth from nonclinical settings. Most (98%, 41/42) studies used a time-based sampling protocol. Among these studies, most (95%, 39/41) prompted youth 2-9 times daily, for a study length ranging from 2-42 days. Sampling frequency and study length did not differ between studies with participants from clinical versus nonclinical settings. Most (88%, 36/41) studies with a time-based sampling protocol defined compliance as the proportion of prompts to which participants responded. In these studies, the weighted average compliance rate was 78.3%. The average compliance rates were not different between studies with clinical (76.9%) and nonclinical (79.2%; P=.29) and studies that used only a mobile-EMA platform (77.4%) and mobile platform plus additional wearable devices (73.0%, P=.36). Among clinical studies, the mean compliance rate was significantly lower in studies that prompted participants 2-3 times (73.5%) or 4-5 times (66.9%) compared with studies with a higher sampling frequency (6+ times: 89.3%). Among nonclinical studies, a higher average compliance rate was observed in studies that prompted participants 2-3 times daily (91.7%) compared with those that prompted participants more frequently (4-5 times: 77.4%; 6+ times: 75.0%). The reported compliance rates did not differ by duration of EMA period among studies from either clinical or nonclinical settings.ConclusionsThe compliance rate among mobile-EMA studies in youth is moderate but suboptimal. Study design may affect protocol compliance differently between clinical and nonclinical participants; including additional wearable devices did not affect...

Background and Aims While there are considerable benefits to Ecological Momentary Assessment (EMA), poor compliance with assessment protocols has been identified as a limitation, particularly in substance users. Our aim was to identify the pooled compliance rate of EMA studies in substance users and examine variables that may influence compliance with EMA protocols, such as the length and frequency of assessments. Design A meta‐analysis and meta‐regression of all possible studies (randomized controlled trials and longitudinal) which incorporated EMA protocols, examining substance use. Setting Studies took place from 1998 to 2017, in numerous countries world‐wide. Participants One hundred and twenty‐six studies were identified, contributing a total of 19 431 participants (52.32% male, mean age = 28.86). Measurements Compliance data, the proportion of responses to the study protocol, were extracted from each study alongside prompt frequency, total length of assessment period, substance use population and device used to administer EMA prompts. Findings The pooled compliance rate across all studies was 75.06% [95% confidence interval (CI) = 72.37%, 77.65%]. There was no evidence that compliance rates were significantly associated with prompt frequency [Q(3) = 7.35, P = 0.061], length of assessment period [Q(2) = 2.40, P = 0.301], substance type [Q(3) = 6.30, P = 0.098] or device administration [Q(4) = 4.28, P = 0.369]. However, dependent samples (69.80%) had lower compliance rates than non‐dependent samples [76.02%; Q(1) = 4.13, P = 0.042]. Conclusions The pooled compliance rate for Ecological Momentary Assessment studies in substance‐using populations from 1998 to 2017 was lower than the recommended rate of 80%, and was not associated with frequency or duration of assessments.

Mobile health (mHealth) is a relatively nascent field, with a variety of technologies being explored and developed. Because of the explosive growth in this field, it is of interest to examine the design, development, and efficacy of various interventions as research becomes available. This systematic review examines current use of mobile health technologies in the prevention or treatment of pediatric obesity to catalogue the types of technologies utilized and the impact of mHealth to improve obesity-related outcomes in youth. Of the 4021 articles that were identified, 41 articles met inclusion criteria. Seventeen intervention studies incorporated mHealth as the primary or supplementary treatment. The remaining articles were in the beginning stages of research development and most often described moderate to high usability, feasibility, and acceptability. Although few effects were observed on outcomes such as body mass index, increases in physical activity, self-reported breakfast and fruit and vegetable consumption, adherence to treatment, and self-monitoring were observed. Findings from this review suggest that mHealth approaches are feasible and acceptable tools in the prevention and treatment of pediatric obesity. The large heterogeneity in research designs highlights the need for more agile scientific processes that can keep up with the speed of technology development.

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New and emerging mobile technologies are providing unprecedented possibilities for understanding and intervening on obesity-related behaviors in real time. However, the mobile health (mHealth) field has yet to catch up with the fast-paced development of technology. Current mHealth efforts in weight management still tend to focus mainly on short message systems (SMS) interventions, rather than taking advantage of real-time sensing to develop Just-In-Time, Adaptive Interventions (JITAIs). This paper will give an overview of the current technology landscape for sensing and intervening on three behaviors that are central to weight management; diet, physical activity, and sleep. Then five studies that really dig into the possibilities that these new technologies afford will be showcased. We conclude with a discussion of hurdles that mHealth obesity research has yet to overcome, and a future-facing discussion.