Job G. Godino

Frailty assessment instruments: Systematic characterization of the uses and contexts of highly-cited instruments.

The medical syndrome of frailty is widely recognized, yet debate remains over how best to measure it in clinical and research settings. This study reviewed the frailty-related research literature by (a) comprehensively cataloging the wide array of instruments that have been utilized to measure frailty, and (b) systematically categorizing the different purposes and contexts of use for frailty instruments frequently cited in the research literature. We identified 67 frailty instruments total; of these, nine were highly-cited (≥ 200 citations). We randomly sampled and reviewed 545 English-language articles citing at least one highly-cited instrument. We estimated the total number of uses, and classified use into eight categories: risk assessment for adverse health outcomes (31% of all uses); etiological studies of frailty (22%); methodology studies (14%); biomarker studies (12%); inclusion/exclusion criteria (10%); estimating prevalence as primary goal (5%); clinical decision-making (2%); and interventional targeting (2%). The most common assessment context was observational studies of older community-dwelling adults. Physical Frailty Phenotype was the most used frailty instrument in the research literature, followed by the Deficit Accumulation Index and the Vulnerable Elders Survey. This study provides an empirical evaluation of the current uses of frailty instruments, which may be important to consider when selecting instruments for clinical or research purposes. We recommend careful consideration in the selection of a frailty instrument based on the intended purpose, domains captured, and how the instrument has been used in the past. Continued efforts are needed to study the validity and feasibility of these instruments.

Reliability and validity of a domain-specific last 7-d sedentary time questionnaire

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Measures of sleep and cardiac functioning during sleep using a multi-sensory commercially-available wristband in adolescents.

To validate measures of sleep and heart rate (HR) during sleep generated by a commercially-available activity tracker against those derived from polysomnography (PSG) in healthy adolescents. Sleep data were concurrently recorded using FitbitChargeHR™ and PSG, including electrocardiography (ECG), during an overnight laboratory sleep recording in 32 healthy adolescents (15 females; age, mean±SD: 17.3±2.5years). Sleep and HR measures were compared between FitbitChargeHR™ and PSG using paired t-tests and Bland-Altman plots. Epoch-by-epoch analysis showed that FitbitChargeHR™ had high overall accuracy (91%), high sensitivity (97%) in detecting sleep, and poor specificity (42%) in detecting wake on a min-to-min basis. On average, FitbitChargeHR™ significantly but negligibly overestimated total sleep time by 8min and sleep efficiency by 1.8%, and underestimated wake after sleep onset by 5.6min (p<0.05). Within FitbitChargeHR™ epochs of sleep, the average HR was 59.3±7.5bpm, which was significantly but negligibly lower than that calculated from ECG (60.2±7.6bpm, p<0.001), with no change in mean discrepancies throughout the night. FitbitChargeHR™ showed good agreement with PSG and ECG in measuring sleep and HR during sleep, supporting its use in assessing sleep and cardiac function in healthy adolescents. Further validation is needed to assess its reliability over prolonged periods of time in ecological settings and in clinical populations.

Impact of Personalised Feedback about Physical Activity on Change in Objectively Measured Physical Activity (the FAB Study): A Randomised Controlled Trial

Background Low levels of physical activity are a major public health concern, and interventions to promote physical activity have had limited success. Whether or not personalised feedback about physical activity following objective measurement motivates behaviour change has yet to be rigorously examined. Methods And Findings: In a parallel group, open randomised controlled trial, 466 healthy adults aged 32 to 54 years were recruited from the ongoing population-based Fenland Study (Cambridgeshire, UK). Participants were randomised to receive either no feedback until the end of the trial (control group, n=120) or one of three different types of feedback: simple, visual, or contextualised (intervention groups, n=346). The primary outcome was physical activity (physical activity energy expenditure (PAEE) in kJ/kg/day and average body acceleration (ACC) in m/s2) measured objectively using a combined heart rate monitor and accelerometer (Actiheart®). The main secondary outcomes included self-reported physical activity, intention to increase physical activity, and awareness of physical activity (the agreement between self-rated and objectively measured physical activity). At 8 weeks, 391 (83.9%) participants had complete physical activity data. The intervention had no effect on objectively measured physical activity (PAEE: β=-0.92, 95% CI=-3.50 to 1.66, p=0.48 and ACC: β=0.01, 95% CI=-0.00 to 0.02, p=0.21), self-reported physical activity (β=-0.39, 95% CI=-1.59 to 0.81), or intention to increase physical activity (β=-0.05, 95% CI=-0.22 to 0.11). However, it was associated with an increase in awareness of physical activity (OR=1.74, 95% CI=1.05 to 2.89). Results did not differ according to the type of feedback. Conclusions Personalised feedback about physical activity following objective measurement increased awareness but did not result in changes in physical activity in the short term. Measurement and feedback may have a role in promoting behaviour change but are ineffective on their own. Trial Registration Current Controlled Trials ISRCTN92551397 http://www.controlled-trials.com/ISRCTN92551397

Effect of communicating genetic and phenotypic risk for type 2 diabetes in combination with lifestyle advice on objectively measured physical activity: protocol of a randomised controlled trial

Type 2 diabetes (T2D) is associated with increased risk of morbidity and premature mortality. Among those at high risk, incidence can be halved through healthy changes in behaviour. Information about genetic and phenotypic risk of T2D is now widely available. Whether such information motivates behaviour change is unknown. We aim to assess the effects of communicating genetic and phenotypic risk of T2D on risk-reducing health behaviours, anxiety, and other cognitive and emotional theory-based antecedents of behaviour change. In a parallel group, open randomised controlled trial, approximately 580 adults born between 1950 and 1975 will be recruited from the on-going population-based, observational Fenland Study (Cambridgeshire, UK). Eligible participants will have undergone clinical, anthropometric, and psychosocial measurements, been genotyped for 23 single-nucleotide polymorphisms associated with T2D, and worn a combined heart rate monitor and accelerometer (Actiheart®) continuously for six days and nights to assess physical activity. Participants are randomised to receive either standard lifestyle advice alone (control group), or in combination with a genetic or a phenotypic risk estimate for T2D (intervention groups). The primary outcome is objectively measured physical activity. Secondary outcomes include self-reported diet, self-reported weight, intention to be physically active and to engage in a healthy diet, anxiety, diabetes-related worry, self-rated health, and other cognitive and emotional outcomes. Follow-up occurs eight weeks post-intervention. Values at follow-up, adjusted for baseline, will be compared between randomised groups. This study will provide much needed evidence on the effects of providing information about the genetic and phenotypic risk of T2D. Importantly, it will be among the first to examine the impact of genetic risk information using a randomised controlled trial design, a population-based sample, and an objectively measured behavioural outcome. Results of this trial, along with recent evidence syntheses of similar studies, should inform policy concerning the availability and use of genetic risk information. Current Controlled Trials ISRCTN09650496BackgroundType 2 diabetes (T2D) is associated with increased risk of morbidity and premature mortality. Among those at high risk, incidence can be halved through healthy changes in behaviour. Information about genetic and phenotypic risk of T2D is now widely available. Whether such information motivates behaviour change is unknown. We aim to assess the effects of communicating genetic and phenotypic risk of T2D on risk-reducing health behaviours, anxiety, and other cognitive and emotional theory-based antecedents of behaviour change.MethodsIn a parallel group, open randomised controlled trial, approximately 580 adults born between 1950 and 1975 will be recruited from the on-going population-based, observational Fenland Study (Cambridgeshire, UK). Eligible participants will have undergone clinical, anthropometric, and psychosocial measurements, been genotyped for 23 single-nucleotide polymorphisms associated with T2D, and worn a combined heart rate monitor and accelerometer (Actiheart®) continuously for six days and nights to assess physical activity. Participants are randomised to receive either standard lifestyle advice alone (control group), or in combination with a genetic or a phenotypic risk estimate for T2D (intervention groups). The primary outcome is objectively measured physical activity. Secondary outcomes include self-reported diet, self-reported weight, intention to be physically active and to engage in a healthy diet, anxiety, diabetes-related worry, self-rated health, and other cognitive and emotional outcomes. Follow-up occurs eight weeks post-intervention. Values at follow-up, adjusted for baseline, will be compared between randomised groups.DiscussionThis study will provide much needed evidence on the effects of providing information about the genetic and phenotypic risk of T2D. Importantly, it will be among the first to examine the impact of genetic risk information using a randomised controlled trial design, a population-based sample, and an objectively measured behavioural outcome. Results of this trial, along with recent evidence syntheses of similar studies, should inform policy concerning the availability and use of genetic risk information.Trial registrationCurrent Controlled Trials ISRCTN09650496

Lifestyle Advice Combined with Personalized Estimates of Genetic or Phenotypic Risk of Type 2 Diabetes, and Objectively Measured Physical Activity: A Randomized Controlled Trial

Background Information about genetic and phenotypic risk of type 2 diabetes is now widely available and is being incorporated into disease prevention programs. Whether such information motivates behavior change or has adverse effects is uncertain. We examined the effect of communicating an estimate of genetic or phenotypic risk of type 2 diabetes in a parallel group, open, randomized controlled trial. Methods and Findings We recruited 569 healthy middle-aged adults from the Fenland Study, an ongoing population-based, observational study in the east of England (Cambridgeshire, UK). We used a computer-generated random list to assign participants in blocks of six to receive either standard lifestyle advice alone (control group, n = 190) or in combination with a genetic (n = 189) or a phenotypic (n = 190) risk estimate for type 2 diabetes (intervention groups). After 8 wk, we measured the primary outcome, objectively measured physical activity (kJ/kg/day), and also measured several secondary outcomes (including self-reported diet, self-reported weight, worry, anxiety, and perceived risk). The study was powered to detect a between-group difference of 4.1 kJ/kg/d at follow-up. 557 (98%) participants completed the trial. There were no significant intervention effects on physical activity (difference in adjusted mean change from baseline: genetic risk group versus control group 0.85 kJ/kg/d (95% CI −2.07 to 3.77, p = 0.57); phenotypic risk group versus control group 1.32 (95% CI −1.61 to 4.25, p = 0.38); and genetic risk group versus phenotypic risk group −0.47 (95% CI −3.40 to 2.46, p = 0.75). No significant differences in self-reported diet, self-reported weight, worry, and anxiety were observed between trial groups. Estimates of perceived risk were significantly more accurate among those who received risk information than among those who did not. Key limitations include the recruitment of a sample that may not be representative of the UK population, use of self-reported secondary outcome measures, and a short follow-up period. Conclusions In this study, we did not observe short-term changes in behavior associated with the communication of an estimate of genetic or phenotypic risk of type 2 diabetes. We also did not observe changes in worry or anxiety in the study population. Additional research is needed to investigate the conditions under which risk information might enhance preventive strategies. (Current Controlled Trials ISRCTN09650496; Date applied: April 4, 2011; Date assigned: June 10, 2011). Trial Registration The trial is registered with Current Controlled Trials, ISRCTN09650496.

Awareness of physical activity in healthy middle-aged adults: a cross-sectional study of associations with sociodemographic, biological, behavioural, and psychological factors

BackgroundInterventions to promote physical activity have had limited success. One reason may be that inactive adults are unaware that their level of physical activity is inadequate and do not perceive a need to change their behaviour. We aimed to assess awareness of physical activity, defined as the agreement between self-rated and objective physical activity, and to investigate associations with sociodemographic, biological, behavioural, and psychological factors.MethodsWe conducted an exploratory, cross-sectional analysis of awareness of physical activity using baseline data collected from 453 participants of the Feedback, Awareness and Behaviour study (Cambridgeshire, UK). Self-rated physical activity was measured dichotomously by asking participants if they believed they were achieving the recommended level of physical activity. Responses were compared to objective physical activity, measured using a combined accelerometer and heart rate monitor (Actiheart®). Four awareness groups were created: overestimators, realistic inactives, underestimators, and realistic actives. Logistic regression was used to assess associations between awareness group and potential correlates.ResultsThe mean (standard deviation) age of participants was 47.0 (6.9) years, 44.4% were male, and 65.1% were overweight (body mass index ≥ 25). Of the 258 (57.0%) who were objectively classified as inactive, 130 (50.4%) misperceived their physical activity by incorrectly stating that they were meeting the guidelines (overestimators). In a multivariable logistic regression model adjusted for age and sex, those with a lower body mass index (Odds Ratio (OR) = 0.95, 95% Confidence Interval (CI) = 0.90 to 1.00), higher physical activity energy expenditure (OR = 1.03, 95% CI = 1.00 to 1.06) and self-reported physical activity (OR = 1.13, 95% CI = 1.07 to 1.19), and lower intention to increase physical activity (OR = 0.69, 95% CI = 0.48 to 0.99) and response efficacy (OR = 0.53, 95% CI = 0.31 to 0.91) were more likely to overestimate their physical activity.ConclusionsOverestimators have more favourable health characteristics than those who are realistic about their inactivity, and their psychological characteristics suggest that they are less likely to change their behaviour. Personalised feedback about physical activity may be an important first step to behaviour change.

Understanding perceived risk of type 2 diabetes in healthy middle-aged adults: A cross-sectional study of associations with modelled risk, clinical risk factors, and psychological factors

AIMS To determine the perceived risk of type 2 diabetes in a sample of healthy middle-aged adults and examine the association between perceived risk and modelled risk, clinical risk factors, and psychological factors theorised to be antecedents of behaviour change. METHODS An exploratory, cross-sectional analysis of perceived risk of type 2 diabetes (framed according to time and in comparison with peers) was conducted using baseline data collected from 569 participants of the Diabetes Risk Communication Trial (Cambridgeshire, UK). Type 2 diabetes risk factors were measured during a health assessment and the Framingham Offspring Diabetes Risk Score was used to model risk. Questionnaires assessed psychological factors including anxiety, diabetes-related worry, behavioural intentions, and other theory-based antecedents of behaviour change. Multivariable regression analyses were used to examine associations between perceived risk and potential correlates. RESULTS Participants with a high perceived risk were at higher risk according to the Framingham Offspring Diabetes Risk Score (p<0.001). Higher perceived risk was observed in those with a higher body fat percentage, lower self-rated health, higher diabetes-related worry, and lower self-efficacy for adhering to governmental recommendations for physical activity (all p<0.001). The framing of perceived risk according to time and in comparison with peers did not influence these results. CONCLUSIONS High perceived risk of type 2 diabetes is associated with higher risk of developing the disease, and a decreased likelihood of engagement in risk-reducing health behaviours. Risk communication interventions should target high-risk individuals with messages about the effectiveness of prevention strategies.

Prevalence of and racial disparities in risk factor control in older adults with diabetes: The atherosclerosis risk in communities study

OBJECTIVE Controversy surrounds appropriate risk factor targets in older adults with diabetes. We evaluated the proportion of older adults with diabetes meeting different targets, focusing on possible differences by race, and assessed whether demographic and clinical characteristics explained disparities. RESEARCH DESIGN AND METHODS We conducted a cross-sectional study of 5,018 participants aged 67–90 years (1,574 with and 3,444 without diagnosed diabetes) who attended visit 5 of the Atherosclerosis Risk in Communities (ARIC) study (2011–2013). Risk factor targets were defined using both stringent (and less stringent) goals: hemoglobin A1c (HbA1c) <7%, <53 mmol/mol (<8%, <64 mmol/mol); LDL cholesterol (LDL-c) <100 mg/dL (<130 mg/dL); and blood pressure (BP) <140/90 mmHg (<150/90 mmHg). We used Poisson regression to obtain prevalence ratios (PRs). RESULTS Most older adults with diabetes met stringent (and less stringent) targets: 72% (90%) for HbA1c, 63% (86%) for LDL-c, and 73% (87%) for BP; but only 35% (68%) met all three. A higher proportion of whites than blacks met targets, however defined. Among people treated for risk factors, racial disparities in prevalence of meeting stringent targets persisted even after adjustment: PRs (whites vs. blacks) were 1.03 (95% CI 0.91, 1.17) for HbA1c, 1.21 (1.09, 1.35) for LDL-c, 1.10 (1.00, 1.21) for BP, and 1.28 (0.99, 1.66) for all three. Results were similar but slightly attenuated using less stringent goals. Black women were less likely than white women to meet targets for BP and all three risk factors; this disparity was not observed in men. CONCLUSIONS Black-white disparities in risk factor control in older adults with diabetes were not fully explained by demographic or clinical characteristics and were greater in women than men. Further study of determinants of these disparities is important.

Relation of Misperception of Healthy Weight to Obesity in Urban Black Men

This study examined the relation between misperception of healthy weight and obesity, as well as moderators of this relation, in a sample of middle‐aged black men. Survey data from 404 mostly immigrant, black males living in greater New York City were collected as part of a larger randomized controlled trial. Data included measures of health status, BMI, perceived healthy weight, and misperception of healthy weight. Misperception of healthy weight was more frequent among obese men (90.2%) than nonobese men (48.7%) (P < 0.001). Mean level of misperception was also significantly higher in obese men than nonobese men (P < 0.001). Health status moderated the relation between misperception of healthy weight and obesity: obese men who felt healthy or who had fewer comorbid conditions had greater misperception of healthy weight than obese men who felt unhealthy or had relatively more comorbid conditions (P < 0.01). Our findings demonstrate that misperception of healthy weight discriminates between obese and nonobese black men, and the magnitude of this relation is exacerbated in obese men who are relatively healthy. Future studies should determine the prevalence of misperception of healthy weight in more diverse populations and identify potential mediators of the relation between misperception of healthy weight and obesity.