The impact of socioeconomic factors, social determinants, and ethnicity on the utilization of glucose sensor technology among persons with diabetes mellitus: a narrative review.

Continuous glucose monitoring (CGM) usage has been shown to improve disease outcomes in people living with diabetes by facilitating better glycemic management. However, previous research has suggested that access to these devices can be influenced by nonmedical factors such as socioeconomic status and ethnicity. It is critical that equitable access to CGM devices is ensured as people from those groups experience poorer diabetes-related health outcomes. In this narrative review, we provide an overview of the various healthcare systems worldwide and how socioeconomic status, social context, and ethnicity shape device usage and the associated health outcomes. In general, we found that having a lower socioeconomic status and belonging to an ethnic minority group negatively impact CGM usage. While financial means proved to be an important mediator in this process, it was not the sole driver as disparities persisted even after adjustment for factors such as income and insurance status. Recommendations to increase CGM usage for people of a lower socioeconomic status and ethnic minorities include increasing the availability of financial, administrative, and educational support, for both patients and healthcare providers. However, recommendations will vary due to local country-specific circumstances, such as reimbursement criteria and healthcare ecosystems.

The effects of income, education, social factors and ethnicity on the use of glucose sensors by people with diabetes mellitus: a narrative review Over the recent years, glucose sensors have transformed the monitoring of glucose levels in people with diabetes. However, access to these devices has been determined by the healthcare systems and the associated rules and regulations, as well as perceptions from providers and patients about who would benefit most from these devices. In this narrative review, we performed an expansive literature search into what is known about factors that negatively impact the access to glucose sensors, and how these factors might be addressed. From this, we learn that, depending on the healthcare system, financial means form a major driver behind the disparities in glucose sensor use. However, factors such as ethnicity and provider and patient perceptions also can negatively affect one's chances to obtain a glucose sensor. Furthermore, we found that a successful program aimed at resolving the found disparities in glucose sensor use must be multi-faceted, and must include measures aimed at financial support, the use of objective and simple criteria for sensor eligibility, as well as educational support for both patients and providers.

Comparing Glucose Outcomes Following Face-to-Face and Remote Initiation of Flash Glucose Monitoring in People Living With Diabetes.

BACKGROUND: When launched, FreeStyle Libre (FSL; a flash glucose monitor) onboarding was mainly conducted face-to-face. The COVID-19 pandemic accelerated a change to online starts with patients directed to online videos such as Diabetes Technology Network UK for education. We conducted an audit to evaluate glycemic outcomes in people who were onboarded face-to-face versus those who were onboarded remotely and to determine the impact of ethnicity and deprivation on those outcomes. METHODS: People living with diabetes who started using FSL between January 2019 and April 2022, had their mode of onboarding recorded and had at least 90 days of data in LibreView with >70% data completion were included in the audit. Glucose metrics (percent time in ranges) and engagement statistics (previous 90-day averages) were obtained from LibreView. Differences between glucose variables and onboarding methods were compared using linear models, adjusting for ethnicity, deprivation, sex, age, percent active (where appropriate), and duration of FSL use. RESULTS: In total, 935 participants (face-to-face 44% [n = 413]; online 56% [n = 522]) were included. There were no significant differences in glycemic or engagement indices between onboarding methods and ethnicities, but the most deprived quintile had significantly lower percent active time (b = -9.20, P = .002) than the least deprived quintile. CONCLUSIONS: Online videos as an onboarding method can be used without significant differences in glucose and engagement metrics. The most deprived group within the audit population had lower engagement metrics, but this did not translate into differences in glucose metrics.

Corrigendum to "The prevalence and long-term health effects of long Covid among hospitalised and non-hospitalised populations: a systematic review and meta-analysis".

[This corrects the article DOI: 10.1016/j.eclinm.2022.101762.].

A Case for Unifying Accelerometry-Derived Movement Behaviors and Tests of Exercise Capacity for the Assessment of Relative Physical Activity Intensity.

Albert Einstein taught us that "everything is relative." People's experience of physical activity (PA) is no different, with "relativism" particularly pertinent to the perception of intensity. Markers of absolute and relative intensities of PA have different but complimentary utilities, with absolute intensity considered best for PA guideline adherence and relative intensity for personalized exercise prescription. Under the paradigm of exercise and PA as medicine, our Technical Note proposes a method of synchronizing accelerometry with the incremental shuttle walking test to facilitate description of the intensity of the free-living PA profile in absolute and relative terms. Our approach is able to generate and distinguish "can do" or "cannot do" (based on exercise capacity) and "does do" or "does not do" (based on relative intensity PA) classifications in a chronic respiratory disease population, facilitating the selection of potential appropriate individually tailored interventions. By synchronizing direct assessments of exercise capacity and PA, clearer insights into the intensity of PA performed during everyday life can be gleaned. We believe the next steps are as follows: (1) to determine the feasibility and effectiveness of using relative and absolute intensities in combination to personalize the approach, (2) to determine its sensitivity to change following interventions (eg, exercise-based rehabilitation), and (3) to explore the use of this approach in healthier populations and in other long-term conditions.

The prevalence and long-term health effects of Long Covid among hospitalised and non-hospitalised populations: A systematic review and meta-analysis.

Background: The aim of this study was to systematically synthesise the global evidence on the prevalence of persistent symptoms in a general post COVID-19 population. Methods: A systematic literature search was conducted using multiple electronic databases (MEDLINE and The Cochrane Library, Scopus, CINAHL, and medRxiv) until January 2022. Studies with at least 100 people with confirmed or self-reported COVID-19 symptoms at ≥28 days following infection onset were included. Patient-reported outcome measures and clinical investigations were both assessed. Results were analysed descriptively, and meta-analyses were conducted to derive prevalence estimates. This study was pre-registered (PROSPERO-ID: CRD42021238247). Findings: 194 studies totalling 735,006 participants were included, with five studies conducted in those <18 years of age. Most studies were conducted in Europe (n = 106) or Asia (n = 49), and the time to follow-up ranged from ≥28 days to 387 days. 122 studies reported data on hospitalised patients, 18 on non-hospitalised, and 54 on hospitalised and non-hospitalised combined (mixed). On average, at least 45% of COVID-19 survivors, regardless of hospitalisation status, went on to experience at least one unresolved symptom (mean follow-up 126 days). Fatigue was frequently reported across hospitalised (28.4%; 95% CI 24.7%-32.5%), non-hospitalised (34.8%; 95% CI 17.6%-57.2%), and mixed (25.2%; 95% CI 17.7%-34.6%) cohorts. Amongst the hospitalised cohort, abnormal CT patterns/x-rays were frequently reported (45.3%; 95% CI 35.3%-55.7%), alongside ground glass opacification (41.1%; 95% CI 25.7%-58.5%), and impaired diffusion capacity for carbon monoxide (31.7%; 95% CI 25.8%-3.2%). Interpretation: Our work shows that 45% of COVID-19 survivors, regardless of hospitalisation status, were experiencing a range of unresolved symptoms at ∼ 4 months. Current understanding is limited by heterogeneous study design, follow-up durations, and measurement methods. Definition of subtypes of Long Covid is unclear, subsequently hampering effective treatment/management strategies. Funding: No funding.

A More Intense Examination of the Intensity of Physical Activity in People Living with Chronic Obstructive Pulmonary Disease: Insights from Threshold-Free Markers of Activity Intensity.

Physical activity (PA) intensity of people living with chronic obstructive pulmonary disease (COPD) is typically evaluated using intensity thresholds developed in younger, healthier populations with greater exercise capacity and free from respiratory symptoms. This study therefore compared (i) PA differences between COPD and non-COPD controls using both traditional intensity thresholds and threshold-free metrics that represent the volume and intensity of the whole PA profile, and (ii) explored the influence of exercise capacity on observed differences. Moderate-to-vigorous physical activity (MVPA), average acceleration (proxy for volume, mg) and intensity distribution of activity were calculated for 76 individuals with COPD and 154 non-COPD controls from wrist-worn ActiGraph accelerometry. PA profiles representing the minimum intensity (acceleration, mg) during the most active accumulated 5-960 min were plotted. Estimated VO2peak and relative intensity were derived from the incremental shuttle walk test distance. Compared to the non-COPD control group, individuals with COPD recorded fewer MVPA minutes (59 vs. 83 min/day), lower overall waking activity (29.1 vs. 36.4 mg) and a poorer waking intensity distribution (-2.73 vs. -2.57). Individuals with COPD also recorded a lower absolute intensity (acceleration, mg) for their most active 5-960 min, but higher intensity relative to their estimated exercise capacity derived from the ISWT. People with COPD have a lower volume and absolute intensity of PA than controls but perform PA at a higher relative intensity. There is a need to move away from absolute intensity thresholds, and towards personalised or relative-intensity thresholds, to reflect reduced exercise capacity in COPD populations.

Sedentary behaviour is associated with heightened cardiovascular, inflammatory and cortisol reactivity to acute psychological stress.

BACKGROUND: Sedentary behaviour is a risk factor for cardiovascular disease (CVD), but the underlying mechanisms remain unclear. Exaggerated psychobiological responses to acute psychological stress increase CVD risk. Sedentary behaviour is associated with characteristics that can predict large psychobiological stress response patterns (e.g., elevated resting blood pressure and systemic inflammation), but it is currently unknown whether sedentary behaviour and stress reactivity are directly linked. The aim of this study was to examine associations between device-assessed sedentary behaviour and measures of stress reactivity. METHODS: Sixty-one healthy adults wore an activPAL (thigh) and ActiGraph (wrist) for seven days to measure habitual levels of sedentary behaviour (mean ± SD = 9.96 ± 1.48 h/day) and moderate-to-vigorous physical activity (mean ± SD = 101.82 ± 42.92 min/day). Participants then underwent stress reactivity testing, where beat-to-beat cardiovascular (e.g., blood pressure, total peripheral resistance), inflammatory (plasma interleukin-6, leukocytes) and salivary cortisol measurements were taken in response to an 8-minute socially evaluative Paced Auditory Serial Addition Test. RESULTS: Higher volumes of daily sedentary behaviour were associated with larger stress responses for diastolic blood pressure (Β=1.264, 95%CI=0.537-1.990, p = .005), total peripheral resistance (Β=40.563, 95%CI=19.310-61.812, p < .001), interleukin-6 (Β=0.219, 95%CI=0.109-0.329, p < .001) and cortisol (Β=1.844, 95%CI=1.139-2.549, p < .001). These findings emerged independent of a priori determined covariates, including daily levels of moderate-to-vigorous physical activity and adiposity. DISCUSSION: Exaggerated stress reactivity is characteristic of high sedentary behaviour and could be a novel mechanism linking sedentary behaviour with CVD. Future work should examine the impact of reducing sedentary behaviour on measures of stress reactivity, as this may have clinical relevance for preventing CVD.

Investigating the Association Between Child Television Viewing and Measured Child Adiposity Outcomes in a Large Nationally Representative Sample of New Zealanders: A Cross-Sectional Study.

BACKGROUND: This study investigates the association between television (TV) viewing and child adiposity and if parental education and child ethnicity moderate this association. METHOD: Cross-sectional, pooled (2013/2014-2016/2017) adult and child New Zealand Health Survey were matched resulting in 13,039 children (2-14 y) and parent dyads. Child TV viewing was estimated using self-reported time for each weekday and weekend. The height (in centimeters), weight (in kilograms), and waist circumference of parents and children were measured. Childhood body mass index and obesity were defined using the International Obesity Task Force cutoff values. Effect modification was assessed by interaction and then by stratifying regression analyses by parent education (low, moderate, and high) and child ethnicity (Asian, European/other, Maori, and Pacific). RESULTS: Overall, watching ≥2 hours TV on average per day in the past week, relative to <2 hours TV viewing, was associated with a higher odds of obesity (adjusted odds ratio = 1.291 [1.108-1.538]), higher body mass index z score (b = 0.123 [0.061-0.187]), and higher waist circumference (b = 0.546 [0.001-1.092]). Interactions considering this association by child ethnicity and parent education revealed little evidence of effect modification. CONCLUSION: While TV viewing was associated with child adiposity, the authors found little support for a moderating role of parental education and child ethnicity.

A digital lifestyle behaviour change intervention for the prevention of type 2 diabetes: a qualitative study exploring intuitive engagement with real-time glucose and physical activity feedback.

BACKGROUND: Mobile health technologies have advanced to now allow monitoring of the acute physiological responses to lifestyle behaviours. Our aim was to explore how people engaged with real-time feedback on their physical activity and glucose levels over several weeks. METHODS: Semi-structured interviews with 26 participants (61.5% female, 56.6 years) at moderate-to-high risk of developing type 2 diabetes were conducted. Interviews were completed after participants took part in an intervention comprising a flash glucose monitor (Freestyle Libre) and a physical activity monitor (Fitbit Charge 2). Purposive sampling ensured representation of ages, genders and group allocations. RESULTS: Inductive thematic analysis revealed how individuals intuitively used, interpreted and acted on feedback from wearable technologies. Six key themes emerged: triggers of engagement with the technologies, links between behaviour and health, lack of confidence, changes to movement behaviours, changes to diet and barriers to lifestyle behaviour change. CONCLUSIONS: Our findings demonstrate that accessing behavioural and physiological feedback can increase self-awareness of how lifestyle impacts short-term health. Some participants noticed a link between the feedback presented by the two devices and changed their behaviour but many did not. Training and educational support, as well as efforts to optimize how feedback is presented to users, are needed to sustain engagement and behaviour change. Extensions of this work to involve people with diabetes are also warranted to explore whether behavioural and physiological feedback in parallel can encourage better diabetes self-management. TRIAL REGISTRATION: ISRCTN Registry, ISRCTN17545949 , 12/05/2017, prospectively registered.

Resistance to data loss from the Freestyle Libre: impact on glucose variability indices and recommendations for data analysis.

Like many wearables, flash glucose monitoring relies on user compliance and is subject to missing data. As recent research is beginning to utilise glucose technologies as behaviour change tools, it is important to understand whether missing data are tolerable. Complete Freestyle Libre data files were amputed to remove 1-6 h of data both at random and over mealtimes (breakfast, lunch, and dinner). Absolute percent errors (MAPE) and intraclass correlation coefficients (ICC) were calculated to evaluate agreement and reliability. Thirty-two (91%) participants provided at least 1 complete day (24 h) of data (age: 44.8 ± 8.6 years, female: 18 (56%); mean fasting glucose: 5.0 ± 0.6 mmol/L). Mean and continuous overall net glycaemic action (CONGA) (60 min) were robust to data loss (MAPE ≤3%). Larger errors were calculated for standard deviation, coefficient of variation (CV) and mean amplitude of glycaemic excursions (MAGE) at increasing missingness (MAPE: 2%-10%, 2%-9%, and 4%-18%, respectively). ICC decreased as missing data increased, with most indicating excellent reliability (>0.9) apart from certain MAGE ICCs, which indicated good reliability (0.84-0.9). Researchers and clinicians should be aware of the potential for larger errors when reporting standard deviation, CV, and MAGE at higher rates of data loss in nondiabetic populations. But where mean and CONGA are of interest, data loss is less of a concern. Novelty: As research now utilises flash glucose monitoring as behavioural change tools in nondiabetic populations, it is important to consider the influence of missing data. Glycaemic variability indices of mean and CONGA are robust to data loss, but standard deviation, CV, and MAGE are influenced at higher rates of missingness.

Examining the Use of Glucose and Physical Activity Self-Monitoring Technologies in Individuals at Moderate to High Risk of Developing Type 2 Diabetes: Randomized Trial.

BACKGROUND: Self-monitoring of behavior (namely, diet and physical activity) and physiology (namely, glucose) has been shown to be effective in type 2 diabetes (T2D) and prediabetes prevention. By combining self-monitoring technologies, the acute physiological consequences of behaviors could be shown, prompting greater consideration to physical activity levels today, which impact the risk of developing diabetes years or decades later. However, until recently, commercially available technologies have not been able to show individuals the health benefits of being physically active. OBJECTIVE: The objective of this study was to examine the usage, feasibility, and acceptability of behavioral and physiological self-monitoring technologies in individuals at risk of developing T2D. METHODS: A total of 45 adults aged ≥40 years and at moderate to high risk of T2D were recruited to take part in a 3-arm feasibility trial. Each participant was provided with a behavioral (Fitbit Charge 2) and physiological (FreeStyle Libre flash glucose monitor) monitor for 6 weeks, masked according to group allocation. Participants were allocated to glucose feedback (4 weeks) followed by glucose and physical activity (biobehavioral) feedback (2 weeks; group 1), physical activity feedback (4 weeks) followed by biobehavioral feedback (2 weeks; group 2), or biobehavioral feedback (6 weeks; group 3). Participant usage (including time spent on the apps and number of glucose scans) was the primary outcome. Secondary outcomes were the feasibility (including recruitment and number of sensor displacements) and acceptability (including monitor wear time) of the intervention. Semistructured qualitative interviews were conducted at the 6-week follow-up appointment. RESULTS: For usage, time spent on the Fitbit and FreeStyle Libre apps declined over the 6 weeks for all groups. Of the FreeStyle Libre sensor scans conducted by participants, 17% (1798/10,582) recorded rising or falling trends in glucose, and 24% (13/45) of participants changed ≥1 of the physical activity goals. For feasibility, 49% (22/45) of participants completed the study using the minimum number of FreeStyle Libre sensors, and a total of 41 sensors were declared faulty or displaced. For acceptability, participants wore the Fitbit for 40.1 (SD 3.2) days, and 20% (9/45) of participants and 53% (24/45) of participants were prompted by email to charge or sync the Fitbit, respectively. Interviews unearthed participant perceptions on the study design by suggesting refinements to the eligibility criteria and highlighting important issues about the usability, wearability, and features of the technologies. CONCLUSIONS: Individuals at risk of developing T2D engaged with wearable digital health technologies providing behavioral and physiological feedback. Modifications are required to both the study and to commercially available technologies to maximize the chances of sustained usage and behavior change. The study and intervention were feasible to conduct and acceptable to most participants. TRIAL REGISTRATION: International Standard Randomized Controlled Trial Number (ISRCTN) 17545949; isrctn.com/ISRCTN17545949.

24-hour accelerometry in COPD: Exploring physical activity, sedentary behavior, sleep and clinical characteristics.

BACKGROUND: The constructs and interdependency of physical behaviors are not well described and the complexity of physical activity (PA) data analysis remains unexplored in COPD. This study examined the interrelationships of 24-hour physical behaviors and investigated their associations with participant characteristics for individuals with mild-moderate airflow obstruction and healthy control subjects. PATIENTS AND METHODS: Vigorous PA (VPA), moderate-to-vigorous PA (MVPA), light PA (LPA), stationary time (ST), average movement intensity (vector magnitude counts per minute), and sleep duration for 109 individuals with COPD and 135 healthy controls were obtained by wrist-worn accelerometry. Principal components analysis (PCA) examined interrelationships of physical behaviors to identify distinct behavioral constructs. Using the PCA component loadings, linear regressions examined associations with participant (+, positive correlation; -, negative correlation), and were compared between COPD and healthy control groups. RESULTS: For both groups PCA revealed ST, LPA, and average movement intensity as distinct behavioral constructs to MVPA and VPA, labeled "low-intensity movement" and "high-intensity movement," respectively. Sleep was also found to be its own distinct behavioral construct. Results from linear regressions supported the identification of distinct behavioral constructs from PCA. In COPD, low-intensity movement was associated with limitations with mobility (-), daily activities (-), health status (+), and body mass index (BMI) (-) independent of high-intensity movement and sleep. High-intensity movement was associated with age (-) and self-care limitations (-) independent of low-intensity movement and sleep. Sleep was associated with gender (0= female, 1= male; [-]), lung function (-), and percentage body fat (+) independent of low-intensity and high-intensity movement. CONCLUSION: Distinct behavioral constructs comprising the 24-hour day were identified as "low-intensity movement," "high-intensity movement," and "sleep" with each construct independently associated with different participant characteristics. Future research should determine whether modifying these behaviors improves health outcomes in COPD.

Using Digital Health Technologies to Understand the Association Between Movement Behaviors and Interstitial Glucose: Exploratory Analysis.

BACKGROUND: Acute reductions in postprandial glucose excursions because of movement behaviors have been demonstrated in experimental studies but less so in free-living settings. OBJECTIVE: The objective of this study was to explore the nature of the acute stimulus-response model between accelerometer-assessed physical activity, sedentary time, and glucose variability over 13 days in nondiabetic adults. METHODS: This study measured physical activity, sedentary time, and interstitial glucose continuously over 13 days in 29 participants (mean age in years: 44.9 [SD 9.1]; female: 59%, 17/29; white: 90%, 26/29; mean body mass index: 25.3 [SD 4.1]) as part of the Sensing Interstitial Glucose to Nudge Active Lifestyles (SIGNAL) research program. Daily minutes spent sedentary, in light activity, and moderate to vigorous physical activity were associated with daily mean glucose, SD of glucose, and mean amplitude of glycemic excursions (MAGE) using generalized estimating equations. RESULTS: After adjustment for covariates, sedentary time in minutes was positively associated with a higher daily mean glucose (mmol/L; beta=0.0007; 95% CI 0.00030-0.00103; P<.001), SD of glucose (mmol/L; beta=0.0006; 95% CI 0.00037-0.00081; P<.001), and MAGE (mmol/L; beta=0.002; 95% CI 0.00131-0.00273; P<.001) for those of a lower fitness. Additionally, light activity was inversely associated with mean glucose (mmol/L; beta=-0.0004; 95% CI -0.00078 to -0.00006; P=.02), SD of glucose (mmol/L; beta=-0.0006; 95% CI -0.00085 to -0.00039; P<.001), and MAGE (mmol/L; beta=-0.002; 95% CI -0.00285 to -0.00146; P<.001) for those of a lower fitness. Moderate to vigorous physical activity was only inversely associated with mean glucose (mmol/L; beta=-0.002; 95% CI -0.00250 to -0.00058; P=.002). CONCLUSIONS: Evidence of an acute stimulus-response model was observed between sedentary time, physical activity, and glucose variability in low fitness individuals, with sedentary time and light activity conferring the most consistent changes in glucose variability. Further work is required to investigate the coupling of movement behaviors and glucose responses in larger samples and whether providing these rich data sources as feedback could induce lifestyle behavior change.

A Novel Algorithm for Determining the Contextual Characteristics of Movement Behaviors by Combining Accelerometer Features and Wireless Beacons: Development and Implementation.

BACKGROUND: Unfortunately, global efforts to promote "how much" physical activity people should be undertaking have been largely unsuccessful. Given the difficulty of achieving a sustained lifestyle behavior change, many scientists are reexamining their approaches. One such approach is to focus on understanding the context of the lifestyle behavior (ie, where, when, and with whom) with a view to identifying promising intervention targets. OBJECTIVE: The aim of this study was to develop and implement an innovative algorithm to determine "where" physical activity occurs using proximity sensors coupled with a widely used physical activity monitor. METHODS: A total of 19 Bluetooth beacons were placed in fixed locations within a multilevel, mixed-use building. In addition, 4 receiver-mode sensors were fitted to the wrists of a roving technician who moved throughout the building. The experiment was divided into 4 trials with different walking speeds and dwelling times. The data were analyzed using an original and innovative algorithm based on graph generation and Bayesian filters. RESULTS: Linear regression models revealed significant correlations between beacon-derived location and ground-truth tracking time, with intraclass correlations suggesting a high goodness of fit (R2=.9780). The algorithm reliably predicted indoor location, and the robustness of the algorithm improved with a longer dwelling time (>100 s; error <10%, R2=.9775). Increased error was observed for transitions between areas due to the device sampling rate, currently limited to 0.1 Hz by the manufacturer. CONCLUSIONS: This study shows that our algorithm can accurately predict the location of an individual within an indoor environment. This novel implementation of "context sensing" will facilitate a wealth of new research questions on promoting healthy behavior change, the optimization of patient care, and efficient health care planning (eg, patient-clinician flow, patient-clinician interaction).

Metadata Correction: Brain Activation in Response to Personalized Behavioral and Physiological Feedback From Self-Monitoring Technology: Pilot Study.

[This corrects the article DOI: 10.2196/jmir.8890.].

Brain Activation in Response to Personalized Behavioral and Physiological Feedback From Self-Monitoring Technology: Pilot Study.

BACKGROUND: The recent surge in commercially available wearable technology has allowed real-time self-monitoring of behavior (eg, physical activity) and physiology (eg, glucose levels). However, there is limited neuroimaging work (ie, functional magnetic resonance imaging [fMRI]) to identify how people's brains respond to receiving this personalized health feedback and how this impacts subsequent behavior. OBJECTIVE: Identify regions of the brain activated and examine associations between activation and behavior. METHODS: This was a pilot study to assess physical activity, sedentary time, and glucose levels over 14 days in 33 adults (aged 30 to 60 years). Extracted accelerometry, inclinometry, and interstitial glucose data informed the construction of personalized feedback messages (eg, average number of steps per day). These messages were subsequently presented visually to participants during fMRI. Participant physical activity levels and sedentary time were assessed again for 8 days following exposure to this personalized feedback. RESULTS: Independent tests identified significant activations within the prefrontal cortex in response to glucose feedback compared with behavioral feedback (P<.001). Reductions in mean sedentary time (589.0 vs 560.0 minutes per day, P=.014) were observed. Activation in the subgyral area had a moderate correlation with minutes of moderate-to-vigorous physical activity (r=0.392, P=.043). CONCLUSION: Presenting personalized glucose feedback resulted in significantly more brain activation when compared with behavior. Participants reduced time spent sedentary at follow-up. Research on deploying behavioral and physiological feedback warrants further investigation.

Sensing interstitial glucose to nudge active lifestyles (SIGNAL): feasibility of combining novel self-monitoring technologies for persuasive behaviour change.

INTRODUCTION: Increasing physical activity (PA) reduces the risk of developing diabetes, highlighting the role of preventive medicine approaches. Changing lifestyle behaviours is difficult and is often predicated on the assumption that individuals are willing to change their lifestyles today to reduce the risk of developing disease years or even decades later. The self-monitoring technologies tested in this study will present PA feedback in real time, parallel with acute physiological data. Presenting the immediate health benefits of being more physically active may help enact change by observing the immediate consequences of that behaviour. The present study aims to assess user engagement with the self-monitoring technologies in individuals at moderate-to-high risk of developing type 2 diabetes. METHODS AND ANALYSIS: 45 individuals with a moderate-to-high risk, aged ≥40 years old and using a compatible smartphone, will be invited to take part in a 7-week protocol. Following 1 week of baseline measurements, participants will be randomised into one of three groups: group 1- glucose feedback followed by biobehavioural feedback (glucose plus PA); group 2-PA feedback followed by biobehavioural feedback; group 3-biobehavioural feedback. A PA monitor and a flash glucose monitor will be deployed during the intervention. Participants will wear both devices throughout the intervention but blinded to feedback depending on group allocation. The primary outcome is the level of participant engagement and will be assessed by device use and smartphone usage. Feasibility will be assessed by the practicality of the technology and screening for diabetes risk. Semistructured interviews will be conducted to explore participant experiences using the technologies. TRIAL REGISTRATION NUMBER: ISRCTN17545949. Registered on 15/05/2017.