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

Purpose: The objective of this study is to examine test-retest reliability, criterion validity, and absolute agreement of a self-report, last 7-d sedentary behavior questionnaire (SIT-Q-7d), which assesses total daily sedentary time as an aggregate of sitting/lying down in five domains (meals, transportation, occupation, nonoccupational screen time, and other sedentary time). Dutch (DQ) and English (EQ) versions of the questionnaire were examined. Methods: Fifty-one Flemish adults (ages 39.4 +/- 11.1 yr) wore a thigh accelerometer (activPAL3 (TM)) and simultaneously kept a domain log for 7 d. The DQ was subsequently completed twice (median test-retest interval: 3.3 wk). Thigh-acceleration sedentary time was log annotated to create comparable domain-specific and total sedentary time variables. Four hundred two English adults (ages 49.6 +/- 7.3 yr) wore a combined accelerometer and HR monitor (Actiheart (R)) for 6 d to objectively measure total sedentary time. The EQ was subsequently completed twice (median test-retest interval: 3.4 wk). In both samples, the questionnaire reference frame overlapped with the criterion measure administration period. All participants had five or more valid days of criterion data, including one or more weekend day. Results: Test-retest reliability (intraclass correlation coefficient (95% CI)) was fair to good for total sedentary time (DQ: 0.68 (0.50-0.81); EQ: 0.53 (0.44-0.62)) and poor to excellent for domain-specific sedentary time (DQ: from 0.36 (0.10-0.57) (meals) to 0.66 (0.46-0.79) (occupation); EQ: from 0.45 (0.35-0.54) (other sedentary time) to 0.76 (0.71-0.81) (meals)). For criterion validity (Spearman rho), significant correlations were found for total sedentary time (DQ: 0.52; EQ: 0.22; all P <0.001). Compared with domain-specific criterion variables (DQ), modest-to-strong correlations were found for domain-specific sedentary time (from 0.21 (meals) to 0.76 (P < 0.001) (screen time)). The questionnaire generally overestimated sedentary time compared with criterion measures. Conclusion: The SIT-Q-7d appears to be a useful tool for ranking individuals in large-scale observational studies examining total and domain-specific sitting

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

BACKGROUND: Interventions 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. METHODS: We 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. RESULTS: The 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. CONCLUSIONS: Overestimators 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

Characterizing and predicting person-specific, day-to-day, fluctuations in walking behavior

Despite the positive health effect of physical activity, one third of the world's population is estimated to be insufficiently active. Prior research has mainly investigated physical activity on an aggregate level over short periods of time, e.g., during 3 to 7 days at baseline and a few months later, post-intervention. To develop effective interventions, we need a better understanding of the temporal dynamics of physical activity. We proposed here an approach to studying walking behavior at "high-resolution" and by capturing the idiographic and day-to-day changes in walking behavior. We analyzed daily step count among 151 young adults with overweight or obesity who had worn an accelerometer for an average of 226 days (similar to 25,000 observations). We then used a recursive partitioning algorithm to characterize patterns of change, here sudden behavioral gains and losses, over the course of the study. These behavioral gains or losses were defined as a 30% increase or reduction in steps relative to each participants' median level of steps lasting at least 7 days. After the identification of gains and losses, fluctuation intensity in steps from each participant's individual time series was computed with a dynamic complexity algorithm to identify potential early warning signals of sudden gains or losses. Results revealed that walking behavior change exhibits discontinuous changes that can be described as sudden gains and losses. On average, participants experienced six sudden gains or losses over the study. We also observed a significant and positive association between critical fluctuations in walking behavior, a form of early warning signals, and the subsequent occurrence of sudden behavioral losses in the next days. Altogether, this study suggests that walking behavior could be well understood under a dynamic paradigm. Results also provide support for the development of "just-in-time adaptive" behavioral interventions based on the detection of early warning signals for sudden behavioral losses.Peer reviewe

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.

BACKGROUND: 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. METHODS: 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. DISCUSSION: 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.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

Verification, Analytical Validation, and Clinical Validation (V3): The Foundation of Determining Fit-for-Purpose for Biometric Monitoring Technologies (BioMeTs)

Digital medicine is an interdisciplinary field, drawing together stakeholders with expertize in engineering, manufacturing, clinical science, data science, biostatistics, regulatory science, ethics, patient advocacy, and healthcare policy, to name a few. Although this diversity is undoubtedly valuable, it can lead to confusion regarding terminology and best practices. There are many instances, as we detail in this paper, where a single term is used by different groups to mean different things, as well as cases where multiple terms are used to describe essentially the same concept. Our intent is to clarify core terminology and best practices for the evaluation of Biometric Monitoring Technologies (BioMeTs), without unnecessarily introducing new terms. We focus on the evaluation of BioMeTs as fit-for-purpose for use in clinical trials. However, our intent is for this framework to be instructional to all users of digital measurement tools, regardless of setting or intended use. We propose and describe a three-component framework intended to provide a foundational evaluation framework for BioMeTs. This framework includes (1) verification, (2) analytical validation, and (3) clinical validation. We aim for this common vocabulary to enable more effective communication and collaboration, generate a common and meaningful evidence base for BioMeTs, and improve the accessibility of the digital medicine field

Text messaging and brief phone calls for weight loss in overweight and obese English- and Spanish-speaking adults: A 1-year, parallel-group, randomized controlled trial.

BACKGROUND:Weight loss interventions based solely on text messaging (short message service [SMS]) have been shown to be modestly effective for short periods of time and in some populations, but limited evidence is available for positive longer-term outcomes and for efficacy in Hispanic populations. Also, little is known about the comparative efficacy of weight loss interventions that use SMS coupled with brief, technology-mediated contact with health coaches, an important issue when considering the scalability and cost of interventions. We examined the efficacy of a 1-year intervention designed to reduce weight among overweight and obese English- and Spanish-speaking adults via SMS alone (ConTxt) or in combination with brief, monthly health-coaching calls. ConTxt offered 2-4 SMS/day that were personalized, tailored, and interactive. Content was theory- and evidence-based and focused on reducing energy intake and increasing energy expenditure. Monthly health-coaching calls (5-10 minutes' duration) focused on goal-setting, identifying barriers to achieving goals, and self-monitoring. METHODS AND FINDINGS:English- and Spanish-speaking adults were recruited from October 2011 to March 2013. A total of 298 overweight (body mass index [BMI] 27.0 to 39.9 kg/m2) adults (aged 21-60 years; 77% female; 41% Hispanic; 21% primarily Spanish speaking; 44% college graduates or higher; 22% unemployed) were randomly assigned (1:1) to receive either ConTxt only (n = 101), ConTxt plus health-coaching calls (n = 96), or standard print materials on weight reduction (control group, n = 101). We used computer-based permuted-block randomization with block sizes of three or six, stratified by sex and Spanish-speaking status. Participants, study staff, and investigators were masked until the intervention was assigned. The primary outcome was objectively measured percent of weight loss from baseline at 12 months. Differences between groups were evaluated using linear mixed-effects regression within an intention-to-treat framework. A total of 261 (87.2%) and 253 (84.9%) participants completed 6- and 12-month visits, respectively. Loss to follow-up did not differ by study group. Mean (95% confidence intervals [CIs]) percent weight loss at 12 months was -0.61 (-1.99 to 0.77) in the control group, -1.68 (-3.08 to -0.27) in ConTxt only, and -3.63 (-5.05 to -2.81) in ConTxt plus health-coaching calls. At 12 months, mean (95% CI) percent weight loss, adjusted for baseline BMI, was significantly different between ConTxt plus health-coaching calls and the control group (-3.0 [-4.99 to -1.04], p = 0.003) but not between the ConTxt-only and the control group (-1.07 [-3.05 to 0.92], p = 0.291). Differences between ConTxt plus health-coaching calls and ConTxt only were not significant (-1.95 [-3.96 to 0.06], p = 0.057). These findings were consistent across other weight-related secondary outcomes, including changes in absolute weight, BMI, and percent body fat at 12 months. Exploratory subgroup analyses suggested that Spanish speakers responded more favorably to ConTxt plus health-coaching calls than English speakers (Spanish contrast: -7.90 [-11.94 to -3.86], p &lt; 0.001; English contrast: -1.82 [-4.03 to 0.39], p = 0.107). Limitations include the unblinded delivery of the intervention and recruitment of a predominantly female sample from a single site. CONCLUSIONS:A 1-year intervention that delivered theory- and evidence-based weight loss content via daily personalized, tailored, and interactive SMS was most effective when combined with brief, monthly phone calls. TRIAL REGISTRATION:ClinicalTrials.gov NCT01171586

Opportunities and challenges in the use of personal health data for health research

Objective: Understand barriers to the use of personal health data (PHD) in research from the perspective of three stakeholder groups: early adopter individuals who track data about their health, researchers who may use PHD as part of their research, and companies that market self-tracking devices, apps or services, and aggregate and manage the data that are generated. Materials and Methods: A targeted convenience sample of 465 individuals and 134 researchers completed an extensive online survey. Thirty-five hourlong semi-structured qualitative interviews were conducted with a subset of 11 individuals and 9 researchers, as well as 15 company/key informants. Results: Challenges to the use of PHD for research were identified in six areas: data ownership; data access for research; privacy; informed consent and ethics; research methods and data quality; and the unpredictable nature of the rapidly evolving ecosystem of devices, apps, and other services that leave “digital footprints.” Individuals reported willingness to anonymously share PHD if it would be used to advance research for the good of the public. Researchers were enthusiastic about using PHD for research, but noted barriers related to intellectual property, licensing, and the need for legal agreements with companies. Companies were interested in research but stressed that their first priority was maintaining customer relationships. Conclusion: Although challenges exist in leveraging PHD for research, there are many opportunities for stakeholder engagement, and experimentation with these data is already taking place. These early examples foreshadow a much larger set of activities with the potential to positively transform how health research is conducted