Andrew Kingsnorth

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.

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.

Influence of muscle mass in the assessment of lower limb strength in COPD: validation of the prediction equation

Absence of established reference values limits application of quadriceps maximal voluntary contraction (QMVC) measurement. The impact of muscle mass inclusion in predictions is unclear. Prediction equations encompassing gender, age and size with (FFM+) and without (FFM−), derived in healthy adults (n=175), are presented and compared in two COPD cohorts recruited from primary care (COPD-PC, n=112) and a complex care COPD clinic (COPD-CC, n=189). Explained variance was comparable between the prediction models (R2: FFM+: 0.59, FFM−: 0.60) as were per cent predictions in COPD-PC (88.8%, 88.3%). However, fat-free mass inclusion reduced the prevalence of weakness in COPD, particularly in COPD-CC where 11.9% fewer were deemed weak.

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).

The influence of South Asian ethnicity on the incremental shuttle walk test in UK adults

The objective of this study was to compare incremental shuttle walking test (ISWT) performance between South Asian and Caucasian British adults, identify predictors of ISWT distance and produce ethnicity-specific reference equations. Data from a mixed gender sample aged 40–75 years from Leicestershire, United Kingdom, were selected for analyses. Analysis of covariance determined differences in ISWT performance between South Asian and Caucasian British ethnic groups. Linear regressions identified predictors of ISWT distance, which determined the reference equations. In total, 144 participants took part in the study (79 South Asian (54 ± 8 years, 71% female) and 65 Caucasian British (58 ± 9 years, 74% female)). Distance walked for the ISWT was shorter for South Asian individuals compared with Caucasian British (451 ± 143 vs. 575 ± 180 m, p < 0.001). The ethnicity-specific reference equations for ISWT distance explained 33–50% of the variance (standard error of the estimate (SEE): 107–119 m) for South Asians and explained 14–58% of the variance (SEE: 121–169 m) for Caucasian British. Ethnicity univariately explained 12.9% of the variance in ISWT distance and was significantly associated with ISWT distance after controlling for age, gender, height, weight, dyspnoea and lung function (B = −70.37; 1 = Caucasian British, 2 = South Asian), uniquely explaining 3.7% of the variance. Predicted values for ISWT performance were lower in South Asian people than in Caucasian British. Ethnicity-specific reference equations should account for this.

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.].

The influence of muscle mass in the assessment of lower limb strength in COPD [Abstract]

Introduction and objectives Lower limb muscle strength measured by Quadriceps Maximal Voluntary Contraction (QMVC) provides valuable functional and prognostic information in people with COPD. Reference equations providing normal values for QMVC have been reported, some requiring measurement of muscle mass. It is unclear whether including muscle mass in the calculation significantly alters predicted values in COPD. We addressed this question by deriving reference equations for QMVC with and without the inclusion of whole body assessment of muscle mass in a cohort of healthy volunteers and subsequently comparing QMVC assessment using these reference equations in two separate cohorts of patients with COPD. Methods Prediction equations were derived through multiple linear regression in a healthy control (HC) group. Age, gender, height and weight were inputted into the first model (FFM– model) and fat-free mass (FFM) added for the other (FFM+ model). The prediction equations were then applied to a Primary Care COPD (PCC) group and Complex Care COPD (CCC) group of patients where percentage predicted values were calculated and weakness determined using a threshold of the lower limit of normal. Results 175 HC subjects (median (IQR) age: 54 (14) years, 31% male) were recruited. The PCC group comprised 87 patients (median (IQR) age: 68 (9) years, 71% male, FEV1 62 (20)% predicted) and the CCC group 189 patients (median (IQR) 66 (12) years, 57% male, FEV1: 29 (16)% predicted). Prediction values for the HC and PCC were similar between the FFM– and FFM+ models as shown in the table. In the CCC percentage predicted values were lower and there were 11.9% more classed as weak by the FFM– model compared to the FFM+ model. Abstract P47 Table 1 QMVC values expressed as percent predicted values and number classed as weak using the FFM− and FFM+ models for the COPD cohorts Healthy control Primary care COPD Complex care COPD n = 175 n = 87 n = 189 FFM− Model- %pred QMVC: 100.3 (24.1) 86.0 (22.0) 54.0 (16.4) Number classed as weak (%): 6 (3.4%) 14 (16.3%) 101 (53.2%) FFM+ Model% pred QMVC: 100.2 (24.1) 86.7 (20.6) 59.2 (17.8) Number classed as weak (%): 8 (4.6%) 10 (11.6%) 78 (41.3%) Mean (SD) values presented as a percentage of the values predicted (%pred) using the FFM– and FFM+ models. Abbreviations: FFM+: fat-free mass included, FFM− fat-free mass not included. Conclusion The inclusion of fat-free mass did not significantly alter prediction of muscle weakness in the healthy cohort. In the COPD cohorts, including FFM in the model altered the proportion classified as having muscle weakness, most notably in the CCC cohort. This is likely to be due to a higher prevalence of muscle wasting in this population which resulted in an underestimate of predicted strength when muscle mass is included in the model.