BackgroundActivity trackers can potentially stimulate users to increase their physical activity behavior. The aim of this study was to examine the reliability and validity of ten consumer activity trackers for measuring step count in both laboratory and free-living conditions.MethodHealthy adult volunteers (n = 33) walked twice on a treadmill (4.8 km/h) for 30 min while wearing ten different activity trackers (i.e. Lumoback, Fitbit Flex, Jawbone Up, Nike+ Fuelband SE, Misfit Shine, Withings Pulse, Fitbit Zip, Omron HJ-203, Yamax Digiwalker SW-200 and Moves mobile application). In free-living conditions, 56 volunteers wore the same activity trackers for one working day. Test-retest reliability was analyzed with the Intraclass Correlation Coefficient (ICC). Validity was evaluated by comparing each tracker with the gold standard (Optogait system for laboratory and ActivPAL for free-living conditions), using paired samples t-tests, mean absolute percentage errors, correlations and Bland-Altman plots.ResultsTest-retest analysis revealed high reliability for most trackers except for the Omron (ICC .14), Moves app (ICC .37) and Nike+ Fuelband (ICC .53). The mean absolute percentage errors of the trackers in laboratory and free-living conditions respectively, were: Lumoback (−0.2, −0.4), Fibit Flex (−5.7, 3.7), Jawbone Up (−1.0, 1.4), Nike+ Fuelband (−18, −24), Misfit Shine (0.2, 1.1), Withings Pulse (−0.5, −7.9), Fitbit Zip (−0.3, 1.2), Omron (2.5, −0.4), Digiwalker (−1.2, −5.9), and Moves app (9.6, −37.6). Bland-Altman plots demonstrated that the limits of agreement varied from 46 steps (Fitbit Zip) to 2422 steps (Nike+ Fuelband) in the laboratory condition, and 866 steps (Fitbit Zip) to 5150 steps (Moves app) in the free-living condition.ConclusionThe reliability and validity of most trackers for measuring step count is good. The Fitbit Zip is the most valid whereas the reliability and validity of the Nike+ Fuelband is low.

MYH9-related disease (MYH9-RD) is a rare autosomal-dominant disorder caused by mutations in the gene for nonmuscle myosin heavy chain IIA (NMMHC-IIA). MYH9-RD is characterized by a considerable variability in clinical evolution: patients present at birth with only thrombocytopenia, but some of them subsequently develop sensorineural deafness, cataract, and/or nephropathy often leading to end-stage renal disease (ESRD). We searched for genotype-phenotype correlations in the largest series of consecutive MYH9-RD patients collected so far (255 cases from 121 families). Association of genotypes with noncongenital features was assessed by a generalized linear regression model. The analysis defined disease evolution associated to seven different MYH9 genotypes that are responsible for 85% of MYH9-RD cases. Mutations hitting residue R702 demonstrated a complete penetrance for early-onset ESRD and deafness. The p.D1424H substitution associated with high risk of developing all the noncongenital manifestations of disease. Mutations hitting a distinct hydrophobic seam in the NMMHC-IIA head domain or substitutions at R1165 associated with high risk of deafness but low risk of nephropathy or cataract. Patients with p.E1841K, p.D1424N, and C-terminal deletions had low risk of noncongenital defects. These findings are essential to patients' clinical management and genetic counseling and are discussed in view of molecular pathogenesis of MYH9-RD.

The survival of microencapsulated islet grafts is limited, even if capsular overgrowth is restricted to a small percentage of the capsules. In search of processes other than overgrowth contributing to graft failure, we have studied the islets in non-overgrown capsules at several time points after allotransplantation in the rat. All recipients of islet allografts became normoglycemic. Grafts were retrieved at 4 and 8 weeks after implantation and at 15.3 +/- 2.3 weeks postimplant, 2 weeks after the mean time period at which graft failure occurred. Overgrowth of capsules was complete within 4 weeks postimplant, and it was usually restricted to <10% of the capsules. During the first 4 weeks of implantation, 40% of the initial number of islets was lost. Thereafter, we observed a decrease in function rather than in numbers of islets, as illustrated by a decline in the ex vivo glucose-induced insulin response. At 4 and 8 weeks postimplant, beta-cell replication was 10-fold higher in encapsulated islets than in islets in the normal pancreas, but these high replication rates were insufficient to prevent a progressive increase in the percentage of nonviable tissue in the islets. Necrosis and not apoptosis proved to be the major cause of cell death in the islets. The necrosis mainly occurred in the center of the islets, which indicates insufficient nutrition as a major causative factor. Our study demonstrates that not only capsular overgrowth but also an imbalance between beta-cell birth and beta-cell death contributes to the failure of encapsulated islet grafts. Our observations indicate that we should focus on finding or creating a transplantation site that, more than the unmodified peritoneal cavity, permits for close contact between the blood and the encapsulated islet tissue.

BackgroundThe combination of self-tracking and persuasive eCoaching in automated interventions is a new and promising approach for healthy lifestyle management.ObjectiveThe aim of this study was to identify key components of self-tracking and persuasive eCoaching in automated healthy lifestyle interventions that contribute to their effectiveness on health outcomes, usability, and adherence. A secondary aim was to identify the way in which these key components should be designed to contribute to improved health outcomes, usability, and adherence.MethodsThe scoping review methodology proposed by Arskey and O’Malley was applied. Scopus, EMBASE, PsycINFO, and PubMed were searched for publications dated from January 1, 2013 to January 31, 2016 that included (1) self-tracking, (2) persuasive eCoaching, and (3) healthy lifestyle intervention.ResultsThe search resulted in 32 publications, 17 of which provided results regarding the effect on health outcomes, 27 of which provided results regarding usability, and 13 of which provided results regarding adherence. Among the 32 publications, 27 described an intervention. The most commonly applied persuasive eCoaching components in the described interventions were personalization (n=24), suggestion (n=19), goal-setting (n=17), simulation (n=17), and reminders (n=15). As for self-tracking components, most interventions utilized an accelerometer to measure steps (n=11). Furthermore, the medium through which the user could access the intervention was usually a mobile phone (n=10). The following key components and their specific design seem to influence both health outcomes and usability in a positive way: reduction by setting short-term goals to eventually reach long-term goals, personalization of goals, praise messages, reminders to input self-tracking data into the technology, use of validity-tested devices, integration of self-tracking and persuasive eCoaching, and provision of face-to-face instructions during implementation. In addition, health outcomes or usability were not negatively affected when more effort was requested from participants to input data into the technology. The data extracted from the included publications provided limited ability to identify key components for adherence. However, one key component was identified for both usability and adherence, namely the provision of personalized content.ConclusionsThis scoping review provides a first overview of the key components in automated healthy lifestyle interventions combining self-tracking and persuasive eCoaching that can be utilized during the development of such interventions. Future studies should focus on the identification of key components for effects on adherence, as adherence is a prerequisite for an intervention to be effective.

Test-retest reliability and validity of activity trackers depends on walking speed. In general, consumer activity trackers perform better at an average and vigorous walking speed than at a slower walking speed.

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BackgroundA lack of physical activity is considered to cause 6% of deaths globally. Feedback from wearables such as activity trackers has the potential to encourage daily physical activity. To date, little research is available on the natural development of adherence to activity trackers or on potential factors that predict which users manage to keep using their activity tracker during the first year (and thereby increasing the chance of healthy behavior change) and which users discontinue using their trackers after a short time.ObjectiveThe aim of this study was to identify the determinants for sustained use in the first year after purchase. Specifically, we look at the relative importance of demographic and socioeconomic, psychological, health-related, goal-related, technological, user experience–related, and social predictors of feedback device use. Furthermore, this study tests the effect of these predictors on physical activity.MethodsA total of 711 participants from four urban areas in France received an activity tracker (Fitbit Zip) and gave permission to use their logged data. Participants filled out three Web-based questionnaires: at start, after 98 days, and after 232 days to measure the aforementioned determinants. Furthermore, for each participant, we collected activity data tracked by their Fitbit tracker for 320 days. We determined the relative importance of all included predictors by using Random Forest, a machine learning analysis technique.ResultsThe data showed a slow exponential decay in Fitbit use, with 73.9% (526/711) of participants still tracking after 100 days and 16.0% (114/711) of participants tracking after 320 days. On average, participants used the tracker for 129 days. Most important reasons to quit tracking were technical issues such as empty batteries and broken trackers or lost trackers (21.5% of all Q3 respondents, 130/601). Random Forest analysis of predictors revealed that the most influential determinants were age, user experience–related factors, mobile phone type, household type, perceived effect of the Fitbit tracker, and goal-related factors. We explore the role of those predictors that show meaningful differences in the number of days the tracker was worn.ConclusionsThis study offers an overview of the natural development of the use of an activity tracker, as well as the relative importance of a range of determinants from literature. Decay is exponential but slower than may be expected from existing literature. Many factors have a small contribution to sustained use. The most important determinants are technical condition, age, user experience, and goal-related factors. This finding suggests that activity tracking is potentially beneficial for a broad range of target groups, but more attention should be paid to technical and user experience–related aspects of activity trackers.

Objective: To systematically assess contemporary knowledge regarding behavioral physical activity interventions including an activity monitor (BPAI1) in adults with overweight or obesity. Methods: PubMed/MEDLINE, Embase, CINAHL, PsycINFO, CENTRAL, and PEDro were searched for eligible full-text articles up to 1 July 2015. Studies eligible for inclusion were (randomized) controlled trials describing physical activity outcomes in adults with overweight or obesity. Methodological quality was independently assessed employing the Cochrane Collaboration's tool for risk of bias. Results: Fourteen studies (1,157 participants) were included for systematic review and 11 for metaanalysis. A positive trend in BPAI1 effects on several measures of physical activity was ascertained compared with both wait list or usual care and behavioral physical activity interventions without an activity monitor (BPAI2). No convincing evidence of BPAI1 effectiveness on weight loss was found compared with BPAI2. Conclusions: Behavioral physical activity interventions with an activity monitor increase physical activity in adults with overweight or obesity. Also, adding an activity monitor to behavioral physical activity interventions appears to increase the effect on physical activity, although current evidence has not yet provided conclusive evidence for its effectiveness. Obesity (2016Obesity ( ) 24, 2078Obesity ( -2091Obesity ( . doi:10.1002 Introduction Worldwide, 1.46 billion adults were overweight and 502 million had obesity in 2008 (1). The global rising prevalence of these conditions is expected to further increase both the health and economic burdens in the following decades (2). Overweight and obesity are frequently caused by a chronic imbalance involving dietary and physical activity patterns (3). Behavioral interventions involving alterations in both physical activity and diet can lead to clinically important weight loss (5% of baseline weight) in adults with overweight or obesity (4). Physical activity should be facilitated in intervention programs to enhance the likelihood of not only successful weight loss and weight maintenance but also for health benefits regardless of weight loss (5). A recent systematic review concluded that physical activity was included in 88% of studies that achieved clinically important weight loss, whereby behavioral training (such as self-monitoring) was included in 92% of these studies (6).Over the previous decades, there has been increasing interest in the therapeutic application of objective measures of self-monitoring. One of the first objective measuring instruments for physical activity was introduced in 1965 with the release of the Japanese manpo-kei pedometer, meaning ''10,000 steps meter'' (7). Currently, devices such as triaxial accelerometers, gyroscopes, and global positioning systems are combined to create activity monitors that are more accurate (8,9) and even integrate behavior change techniques (BCTs) such as social support, prompts/cues, rewards, and behavioral outcome self-monitoring (...