OBJECTIVES: Regular physical activity (PA) and reduced sedentary behaviour (SB) have been associated with positive health outcomes, but many older adults do not comply with the current recommendations. Sensor-triggered ecological momentary assessment (EMA) studies allow capturing real-time data during or immediately after PA or SB, which can yield important insights into these behaviours. Despite the promising potential of sensor-triggered EMA, this methodology is still in its infancy. Addressing methodological challenges in sensor-triggered EMA studies is essential for improving protocol adherence and enhancing validity. Therefore, this study aimed to examine (1) the patterns in sensor-triggered EMA protocol adherence (eg, compliance rates), (2) the impact of specific settings (eg, event duration) on the number of prompted surveys, and (3) participants' experiences with engaging in a sensor-triggered EMA study. DESIGN: Two longitudinal, sensor-triggered EMA studies-one focused on PA and the other on SB-were conducted using similar methodologies from February to October 2022. Participants' steps were monitored for seven days using a Fitbit activity tracker, which automatically prompted an EMA survey through the HealthReact smartphone application when specified (in)activity thresholds were reached. After the monitoring period, qualitative interviews were conducted. Data from both studies were merged. SETTING: The studies were conducted among community-dwelling Belgian older adults. PARTICIPANTS: The participants had a median age of 72 years, with 54.17% being females. The PA study included 88 participants (four dropped out), while the SB study included 76 participants (seven dropped out). PRIMARY AND SECONDARY OUTCOME MEASURES: Descriptive methods and generalised logistic mixed models were employed to analyse EMA adherence patterns. Simulations were conducted to assess the impact of particular settings on the number of prompted EMA surveys. Additionally, qualitative interview data were transcribed verbatim and thematically analysed using NVivo. RESULTS: Participants responded to 81.22% and 79.10% of the EMA surveys in the PA and SB study, respectively. The confirmation rate, defined as the percentage of EMA surveys in which participants confirmed the detected behaviour, was 94.16% for PA and 72.40% for SB. Logistic mixed models revealed that with each additional day in the study, the odds of responding to the EMA survey increased significantly by 1.59 times (OR=1.59, 95% CI: 1.36 to 1.86, p<0.01) in the SB study. This effect was not observed in the PA study. Furthermore, time in the study did not significantly impact the odds of participants confirming to be sedentary (OR=0.97, 95% CI: 0.92 to 1.02, p=0.28). However, it significantly influenced the odds of confirming PA (OR: 0.81, 95% CI: 0.68 to 0.97, p=0.02), with the likelihood of confirming decreasing by 19% with each additional day in the study. Furthermore, a one-minute increase in latency (ie, time between last syncing and starting the EMA survey) in the PA study decreased the odds of the participant confirming to be physically active by 20% (OR: 0.80, 95% CI: 0.72 to 0.89, p<0.01). Simulations of the specific EMA settings revealed that reducing the event duration and shorter minimum time intervals between prompts increased the number of EMA surveys. Overall, most participants found smartphone usage to be feasible and rated the HealthReact app as user-friendly. However, some reported issues, such as not hearing the notification, receiving prompts at an inappropriate time and encountering technical issues. While the majority reported that their behaviour remained unchanged due to study participation, some noted an increased awareness of their habits and felt more motivated to engage in PA. CONCLUSIONS: This study demonstrates the potential of sensor-triggered EMA to capture real-time data on PA and SB among older adults, showing strong adherence potential with compliance rates of approximately 80%. The SB study had lower confirmation rates than the PA study, due to technical issues and discrepancies between self-perception and device-based measurements. Practical recommendations were provided for future studies, including improvements in survey timing, technical reliability and strategies to reduce latency.

• Klíčová slova
• Aging, Behavior, Feasibility Studies, Observational Study, PUBLIC HEALTH, eHealth,
• MeSH
• cvičení * MeSH
• fitness náramky MeSH
• lidé MeSH
• longitudinální studie MeSH
• okamžité posouzení v přirozeném prostředí * MeSH
• samostatný způsob života * MeSH
• sedavý životní styl * MeSH
• senioři nad 80 let MeSH
• senioři MeSH
• Check Tag
• lidé MeSH
• mužské pohlaví MeSH
• senioři nad 80 let MeSH
• senioři MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• Geografické názvy
• Belgie MeSH

A perceived 'lack of time' is consistently the most commonly reported barrier to exercise. However, the term fails to capture the multifaceted nature of time-related factors. Recognising the need for a more comprehensive analysis of 'lack of time' as a barrier to exercise, the aim of this study was to develop the exercise participation explained in relation to time (EXPERT) model. The model was developed through a sequential process including (1) an umbrella literature review of time as a barrier, determinant, and correlate of physical activity; (2) a targeted review of existing temporal models; (3) drafting the model and refining it via discussions between eight authors; (4) a three-round Delphi process with eight panel members; and (5) consultations with seven experts and potential end-users. The final EXPERT model includes 31 factors within four categories: (1) temporal needs and preferences for exercise (ie, when and how long does an individual need/want to exercise), (2) temporal autonomy for exercise (ie, autonomy in scheduling free time for exercise), (3) temporal conditions for exercise (ie, available time for exercise) and (4) temporal dimensions of exercise (ie, use of time for exercise). Definitions, examples and possible survey questions are presented for each factor. The EXPERT model provides a comprehensive framework for understanding the multi-dimensional nature of 'time' as it relates to exercise participation. It moves beyond the simplistic notion of 'lack of time' and delves into the complexity of time allocation in the context of exercise. Empirical and cross-cultural validations of the model are warranted.

• Klíčová slova
• Exercise, Health promotion, Physical activity, Preventive Medicine, Sports medicine,
• MeSH
• časové faktory MeSH
• cvičení * fyziologie   MeSH
• delfská metoda * MeSH
• lidé MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• přehledy MeSH

OBJECTIVE: This study aims to assess the suitability of Fitbit devices for real-time physical activity (PA) and sedentary behaviour (SB) monitoring in the context of just-in-time adaptive interventions (JITAIs) and event-based ecological momentary assessment (EMA) studies. METHODS: Thirty-seven adults (18-65 years) and 32 older adults (65+) from Belgium and the Czech Republic wore four devices simultaneously for 3 days: two Fitbit models on the wrist, an ActiGraph GT3X+ at the hip and an ActivPAL at the thigh. Accuracy measures included mean (absolute) error and mean (absolute) percentage error. Concurrent validity was assessed using Lin's concordance correlation coefficient and Bland-Altman analyses. Fitbit's sensitivity and specificity for detecting stepping events across different thresholds and durations were calculated compared to ActiGraph, while ROC curve analyses identified optimal Fitbit thresholds for detecting sedentary events according to ActivPAL. RESULTS: Fitbits demonstrated validity in measuring steps on a short time scale compared to ActiGraph. Except for stepping above 120 steps/min in older adults, both Fitbit models detected stepping bouts in adults and older adults with sensitivities and specificities exceeding 87% and 97%, respectively. Optimal cut-off values for identifying prolonged sitting bouts achieved sensitivities and specificities greater than 93% and 89%, respectively. CONCLUSIONS: This study provides practical insights into using Fitbit devices in JITAIs and event-based EMA studies among adults and older adults. Fitbits' reasonable accuracy in detecting short bouts of stepping and SB makes them suitable for triggering JITAI prompts or EMA questionnaires following a PA or SB event of interest.

• Klíčová slova
• Fitbit, JITAI, accelerometry, ecological momentary assessment, event-based ecological momentary assessment (EMA), just-in-time adaptive intervention, physical activity, sedentary behaviour, step, wearable,
• Publikační typ
• časopisecké články MeSH

BACKGROUND: The escalating global prevalence of type 2 diabetes and prediabetes presents a major public health challenge. Physical activity plays a critical role in managing (pre)diabetes; however, adherence to physical activity recommendations remains low. The ENERGISED trial was designed to address these challenges by integrating mHealth tools into the routine practice of general practitioners, aiming for a significant, scalable impact in (pre)diabetes patient care through increased physical activity and reduced sedentary behaviour. METHODS: The mHealth intervention for the ENERGISED trial was developed according to the mHealth development and evaluation framework, which includes the active participation of (pre)diabetes patients. This iterative process encompasses four sequential phases: (a) conceptualisation to identify key aspects of the intervention; (b) formative research including two focus groups with (pre)diabetes patients (n = 14) to tailor the intervention to the needs and preferences of the target population; (c) pre-testing using think-aloud patient interviews (n = 7) to optimise the intervention components; and (d) piloting (n = 10) to refine the intervention to its final form. RESULTS: The final intervention comprises six types of text messages, each embodying different behaviour change techniques. Some of the messages, such as those providing interim reviews of the patients' weekly step goal or feedback on their weekly performance, are delivered at fixed times of the week. Others are triggered just in time by specific physical behaviour events as detected by the Fitbit activity tracker: for example, prompts to increase walking pace are triggered after 5 min of continuous walking; and prompts to interrupt sitting following 30 min of uninterrupted sitting. For patients without a smartphone or reliable internet connection, the intervention is adapted to ensure inclusivity. Patients receive on average three to six messages per week for 12 months. During the first six months, the text messaging is supplemented with monthly phone counselling to enable personalisation of the intervention, assistance with technical issues, and enhancement of adherence. CONCLUSIONS: The participatory development of the ENERGISED mHealth intervention, incorporating just-in-time prompts, has the potential to significantly enhance the capacity of general practitioners for personalised behavioural counselling on physical activity in (pre)diabetes patients, with implications for broader applications in primary care.

• Klíčová slova
• Behaviour change techniques, Fitbit, Just-in-time adaptive intervention (JITAI), Participatory development, Phone counselling, Primary care, Self-regulation theory, Text messages, Walking, Wearables,
• MeSH
• cvičení MeSH
• diabetes mellitus 2. typu * prevence a kontrola   epidemiologie   MeSH
• lidé MeSH
• mobilní telefon * MeSH
• praktické lékařství * MeSH
• prediabetes * terapie   MeSH
• sedavý životní styl MeSH
• telemedicína * metody   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

BACKGROUND: The growing number of patients with type 2 diabetes and prediabetes is a major public health concern. Physical activity is a cornerstone of diabetes management and may prevent its onset in prediabetes patients. Despite this, many patients with (pre)diabetes remain physically inactive. Primary care physicians are well-situated to deliver interventions to increase their patients' physical activity levels. However, effective and sustainable physical activity interventions for (pre)diabetes patients that can be translated into routine primary care are lacking. METHODS: We describe the rationale and protocol for a 12-month pragmatic, multicentre, randomised, controlled trial assessing the effectiveness of an mHealth intervention delivered in general practice to increase physical activity and reduce sedentary behaviour of patients with prediabetes and type 2 diabetes (ENERGISED). Twenty-one general practices will recruit 340 patients with (pre)diabetes during routine health check-ups. Patients allocated to the active control arm will receive a Fitbit activity tracker to self-monitor their daily steps and try to achieve the recommended step goal. Patients allocated to the intervention arm will additionally receive the mHealth intervention, including the delivery of several text messages per week, with some of them delivered just in time, based on data continuously collected by the Fitbit tracker. The trial consists of two phases, each lasting six months: the lead-in phase, when the mHealth intervention will be supported with human phone counselling, and the maintenance phase, when the intervention will be fully automated. The primary outcome, average ambulatory activity (steps/day) measured by a wrist-worn accelerometer, will be assessed at the end of the maintenance phase at 12 months. DISCUSSION: The trial has several strengths, such as the choice of active control to isolate the net effect of the intervention beyond simple self-monitoring with an activity tracker, broad eligibility criteria allowing for the inclusion of patients without a smartphone, procedures to minimise selection bias, and involvement of a relatively large number of general practices. These design choices contribute to the trial's pragmatic character and ensure that the intervention, if effective, can be translated into routine primary care practice, allowing important public health benefits. TRIAL REGISTRATION: ClinicalTrials.gov (NCT05351359, 28/04/2022).

• Klíčová slova
• Active control, Ecological Momentary Assessment (EMA), Fitbit, Just-in-time adaptive intervention (JITAI), Micro-randomisation, Phone counselling, Text messages, Primary care, Self-monitoring, Step-count,
• MeSH
• cvičení MeSH
• diabetes mellitus 2. typu * prevence a kontrola   MeSH
• lidé MeSH
• multicentrické studie jako téma MeSH
• pragmatické klinické studie jako téma MeSH
• praktické lékařství * MeSH
• prediabetes * terapie   MeSH
• randomizované kontrolované studie jako téma MeSH
• sedavý životní styl MeSH
• telemedicína * MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• protokol klinické studie MeSH