Background: Ecological momentary assessment (EMA) is a method of collecting real-time data based on repeated measures and observations that take place in participant’s daily environment. EMA has many advantages over more traditional, retrospective questionnaires. However, EMA faces some challenges to reach its full potential. The aims of this systematic review are to (1) investigate whether and how content validity of the items (i.e. the specific questions that are part of a larger EMA questionnaire) used in EMA studies on physical activity and sedentary behaviour was assessed, and (2) provide an overview of important methodological considerations of EMA in measuring physical activity and sedentary behaviour. Methods: Thirty papers (twenty unique studies) were systematically reviewed and variables were coded and analysed within the following 4 domains: (1) Content validity, (2) Sampling approach, (3) Data input modalities and (4) Degree of EMA completion. Results: Only about half of the studies reported the specific items (n = 12) and the source of the items (n = 11). None of the studies specifically assessed the content validity of the items used. Only a minority (n = 5) of the studies reported any training, and one tested the comprehensibility of the EMA items. A wide variability was found in the design and methodology of the EMA. A minority of the studies (n = 7) reported a rationale for the used prompt frequency, time selection, and monitoring period. Retrospective assessment periods varied from ‘now’ to ‘in the last 3.5 hours’. In some studies there was a possibility to delay (n = 6) or deactivate (n = 10) the prompt, and some provided reminders after the first prompt (n = 9). Conclusions: Almost no EMA studies reported the content validation of the items used. We recommend using the COSMIN checklist (COnsensus-based Standards for the selection of health Measurement INstruments) to report on the content validity of EMA items. Furthermore, as often no rationale was provided for several methodological decisions, the following three recommendations are made. First, provide a rationale for choosing the sampling modalities. Second, to ensure assessment ‘in the moment’, think carefully about the retrospective assessment period, reminders, and deactivation of the prompt. Third, as high completion rates are important for representativeness of the data and generalizability of the findings, report completion rates. Trial registration: This review is registered in PROSPERO, the International prospective register of systematic reviews (registration number: CRD42017077996).

Background: Wearables for monitoring physical activity (PA) are increasingly popular. These devices are not only used by consumers to monitor their own levels of PA but also by researchers to track the behavior of large samples. Consequently, it is important to explore how accurately PA can be tracked via these devices. Objectives: The aim of this study was, therefore, to investigate convergent validity of 3 Android Wear smartwatches—Polar M600 (Polar Electro Oy, Kempele, Finland), Huawei Watch (Huawei Technologies Co, Ltd, Shenzhen, Guangdong, China), Asus Zenwatch3 (AsusTek Computer Inc, Taipei, Taiwan)—and Fitbit Charge with an ActiGraph accelerometer for measuring steps and moderate to vigorous physical activity (MVPA) on both a day level and 15-min level. Methods: A free-living protocol was used in which 36 adults engaged in usual daily activities over 2 days while wearing 2 different wearables on the nondominant wrist and an ActiGraph GT3X+ accelerometer on the hip. Validity was evaluated on both levels by comparing each wearable with the ActiGraph GT3X+ accelerometer using correlations and Bland-Altman plots in IBM SPSS 24.0. Results: On a day level, all devices showed strong correlations (Spearman r=.757-.892) and good agreement (interclass correlation coefficient, ICC=.695-.885) for measuring steps, whereas moderate correlations (Spearman r=.557-.577) and low agreement (ICC=.377-.660) for measuring MVPA. Bland-Altman revealed a systematic overestimation of the wearables for measuring steps but a variation between over- and undercounting of MVPA. On a 15-min level, all devices showed strong correlations (Spearman r=.752-.917) and good agreement (ICC=.792-.887) for measuring steps, whereas weak correlations (Spearman r=.116-.208) and low agreement (ICC=.461-.577) for measuring MVPA. Bland-Altman revealed a systematic overestimation of the wearables for steps but under- or overestimation for MVPA depending on the device. Conclusions: In sum, all 4 consumer-level devices can be considered accurate step counters in free-living conditions. This study, however, provides evidence of systematic bias for all devices in measurement of MVPA. The results on a 15-min level also indicate that these devices are not sufficiently accurate to provide correct real-time feedback.

Background: Wearable trackers for monitoring physical activity (PA) and total sleep time (TST) are increasingly popular. These devices are used not only by consumers to monitor their behavior but also by researchers to track the behavior of large samples and by health professionals to implement interventions aimed at health promotion and to remotely monitor patients. However, high costs and accuracy concerns may be barriers to widespread adoption. Objective: This study aimed to investigate the concurrent validity of 6 low-cost activity trackers for measuring steps, moderate-to-vigorous physical activity (MVPA), and TST: Geonaut On Coach, iWown i5 Plus, MyKronoz ZeFit4, Nokia GO, VeryFit 2.0, and Xiaomi MiBand 2. Methods: A free-living protocol was used in which 20 adults engaged in their usual daily activities and sleep. For 3 days and 3 nights, they simultaneously wore a low-cost tracker and a high-cost tracker (Fitbit Charge HR) on the nondominant wrist. Participants wore an ActiGraph GT3X+ accelerometer on the hip at daytime and a BodyMedia SenseWear device on the nondominant upper arm at nighttime. Validity was assessed by comparing each tracker with the ActiGraph GT3X+ and BodyMedia SenseWear using mean absolute percentage error scores, correlations, and Bland-Altman plots in IBM SPSS 24.0. Results: Large variations were shown between trackers. Low-cost trackers showed moderate-to-strong correlations (Spearman r=0.53-0.91) and low-to-good agreement (intraclass correlation coefficient [ICC]=0.51-0.90) for measuring steps. Weak-to-moderate correlations (Spearman r=0.24-0.56) and low agreement (ICC=0.18-0.56) were shown for measuring MVPA. For measuring TST, the low-cost trackers showed weak-to-strong correlations (Spearman r=0.04-0.73) and low agreement (ICC=0.05-0.52). The Bland-Altman plot revealed a variation between overcounting and undercounting for measuring steps, MVPA, and TST, depending on the used low-cost tracker. None of the trackers, including Fitbit (a high-cost tracker), showed high validity to measure MVPA. Conclusions: This study was the first to examine the concurrent validity of low-cost trackers. Validity was strongest for the measurement of steps; there was evidence of validity for measurement of sleep in some trackers, and validity for measurement of MVPA time was weak throughout all devices. Validity ranged between devices, with Xiaomi having the highest validity for measurement of steps and VeryFit performing relatively strong across both sleep and steps domains. Low-cost trackers hold promise for monitoring and measurement of movement and sleep behaviors, both for consumers and researchers.

Background: Electronic health (eHealth) and mobile health (mHealth) interventions have the potential to tackle the worldwide problem of physical inactivity. However, they often suffer from large attrition rates. Consequently, feasibility and acceptability of interventions have become important matters in the creation of e- and mHealth interventions. The aim of this study was to evaluate participants’ opinions regarding acceptability and feasibility of a self-regulation, app-based intervention called ‘MyDayPlan’. ‘MyDayPlan’ provides an innovative daily cycle providing several self-regulation techniques throughout the day that guide users towards an active lifestyle via various self-regulation techniques. Methods: Semi-structured interviews were conducted with 20 adults after using the app for 2 weeks. A directed content analysis was performed using NVivo Software. Results: ‘MyDayPlan’ was well-received and seems to be feasible and acceptable with inactive adults. The straightforward lay out and ease of use of the app were appreciated. Furthermore, the incorporation of the techniques ‘action planning’, and ‘prompting review of behavioral goals’ was positively evaluated. However, the users gave some recommendations: implementation of activity trackers to self-monitor physical activity could be of added value. Furthermore, increasing intuitiveness by minimizing text input and providing more preprogrammed options could further increase the ease of use. Finally, users indicated that they would benefit from more guidance during the “coping planning” component (barrier identification/problem solving), for example by receiving more tailored examples. Conclusions: Based on these findings, adaptations will be made to the ‘MyDayPlan’ app before evaluating its effectiveness. Furthermore, involving potential end users and evaluating acceptability and feasibility during the development of an e- and mHealth intervention is key. Also, creating interventions with a large ease of use and straightforward layout that provides tailored support during action and coping planning is key.

Physical inactivity is one of the major risk factors for poor health in the world. Therefore, effective interventions that promote physical activity are needed. Hence, we developed an eHealth intervention for adults, i.e., ‘MyPlan 1.0’, which includes self-regulation techniques for behaviour change. This study examined the effect of ‘MyPlan 1.0’ on physical activity (PA) levels in general practice. 615 adults (≥18 years) were recruited in 19 Flemish general practices, for the intervention group (n = 328) or for the wait-list control group (n = 183). Participants in the intervention group received the web-based intervention ‘MyPlan 1.0’ and were prompted to discuss their personal advice/action plan with their general practitioner. Participants in the wait-list control group only received general advice from the website. Self-reported physical activity was assessed with the International Physical Activity Questionnaire (IPAQ) at baseline and after one month. A three-level (general practice, adults, time) regression analysis was conducted in MLwiN. Significant intervention effects were found for total PA and moderate to vigorous PA with an increase for the intervention group compared to a decrease in the control condition. However, there was a high dropout rate in the intervention group (76%) and the wait-list control group (57%). Our self-regulation intervention was effective in increasing physical activity levels in adults. Future studies should consider strategies to prevent the large dropout from participants.

Background: e- and mHealth interventions using self-regulation techniques like action and coping planning have the potential to tackle the worldwide problem of physical inactivity. However, they often use one-week self-regulation cycles, providing support toward an active lifestyle on a weekly basis. This may be too long to anticipate on certain contextual factors that may fluctuate from day to day and may influence physical activity. Consequently, the formulated action and coping plans often lack specificity and instrumentality, which may decrease effectiveness of the intervention. The aim of this study was to evaluate effectiveness of a self-regulation, app-based intervention called ‘MyDayPlan’. “MyDayPlan’ provides an innovative daily cycle in which users are guided towards more physical activity via self-regulation techniques such as goal setting, action planning, coping planning and self-monitoring of behaviour. Methods: An ABAB single-case design was conducted in 35 inactive adults between 18 and 58 years (M = 40 years). The A phases (A1 and A2) were the control phases in which the ‘MyDayPlan’ intervention was not provided. The B phases (B1 and B2) were the intervention phases in which ‘MyDayPlan’ was used on a daily basis. The length of the four phases varied within and between the participants. Each phase lasted a minimum of 5 days and the total study lasted 32 days for each participant. Participants wore a Fitbit activity tracker during waking hours to assess number of daily steps as an outcome. Single cases were aggregated and data were analysed using multilevel models to test intervention effects and possible carry-over effects. Results: Results showed an average intervention effect with a significant increase in number of daily steps from the control to intervention phases for each AB combination. From A1 to B1, an increase of 1424 steps (95% CI [775.42, 2072.32], t (1082) = 4.31,p < .001), and from A2 to B2, an increase of 1181 steps (95% CI [392.98, 1968.16], t (1082) = 2.94, p = .003) were found. Furthermore, the number of daily steps decreased significantly (1134 steps) when going from the first intervention phase (B1) to the second control phase (A2) (95% CI [− 1755.60, − 512.38], t (1082) = − 3.58, p < .001). We found no evidence for a difference in trend between the two control (95% CI [− 114.59, 197.99], t (1078) = .52, p = .60) and intervention phases (95% CI [− 128.79,284.22], t (1078) = .74, p = .46). This reveals, in contrast to what was hypothesized, no evidence for a carry-over effect after removing the ‘MyDayPlan’ app after the first intervention phase (B1). Conclusion: This study adds evidence that the self-regulation mHealth intervention, ‘MyDayPlan’ has the capacity to positively influence physical activity levels in an inactive adult population. Furthermore, this study provides evidence for the potential of interventions adopting a daily self-regulation cycle in general.