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

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.]

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 re-examining their approaches. One such approach is to focus on understanding the context of the lifestyle behavior (i.e., 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 (e.g., patient-clinician flow, patient-clinician interaction)

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

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

Enhancing clinical and public health interpretation of accelerometer-assessed physical activity with age-referenced values based on UK Biobank data

Purpose: Higher accelerometer-assessed volume and intensity of physical activity (PA) have been associated with a longer life expectancy but can be difficult to translate into recommended doses of PA. We aimed to: (a) improve interpretability by producing UK Biobank age-referenced centiles for PA volume and intensity; (b) inform public-health messaging by examining how adding recommended quantities of moderate and vigorous PA affect PA volume and intensity. Methods: 92,480 UK-Biobank participants aged 43-80 with wrist-worn accelerometer data were included. Average acceleration and intensity gradient were derived as proxies for PA volume and intensity. We generated sex-specific centile curves using Generalized Additive Models for Location Scale and Shape (GAMLSS) and modelled the effect of adding moderate (walking) or vigorous (running) activity on the combined change in the volume and intensity centiles (change in PA profile). Results: In men, volume was lower as age increased while intensity was lower after age 55; in women, both volume and intensity were lower as age increased. Adding 150-minutes moderate PA weekly - 5 x 30-minutes walking - increased the PA profile by 4 percentage points. Defining moderate PA as brisk walking ~doubled the increase (9 percentage points) while 75-minutes vigorous PA weekly (5 x 15-minutes running) trebled the increase (13 percentage points). Conclusion: These UK Biobank reference centiles provide a benchmark for interpretation of accelerometer data. Application of our translational methods demonstrate that meeting PA guidelines through shorter duration vigorous activity is more beneficial to the PA profile (volume and intensity) than longer duration moderate activity

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

Mark W Orme,1&ndash;3 Michael C Steiner,1&ndash;3 Mike D Morgan,1 Andrew P Kingsnorth,2,3 Dale W Esliger,2&ndash;4 Sally J Singh,1&ndash;3,* Lauren B Sherar2&ndash;4,* 1Centre for Exercise and Rehabilitation Science, NIHR Leicester Biomedical Research Centre &ndash; Respiratory, Leicester, UK; 2School of Sport, Exercise and Health Sciences, Loughborough University, Loughborough, UK; 3National Centre for Sport and Exercise Medicine, Loughborough, UK; 4NIHR Leicester Biomedical Research Centre, Leicester, UK *These authors contributed equally to this work 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&ndash;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 &ldquo;low-intensity movement&rdquo; and &ldquo;high-intensity movement,&rdquo; 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 &ldquo;low-intensity movement,&rdquo; &ldquo;high-intensity movement,&rdquo; and &ldquo;sleep&rdquo; with each construct independently associated with different participant characteristics. Future research should determine whether modifying these behaviors improves health outcomes in COPD. Keywords: accelerometry, COPD, physical activity, principal components analysis, sedentary behavio

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

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.</p