Background Regular physical activity (PA) is an essential component of a successful type 2 diabetes treatment. However, despite the manifest evidence for the numerous health benefits of regular PA, most patients with type 2 diabetes remain inactive, often due to low motivation and lack of PA enjoyment. A recent and promising approach to help overcome these PA barriers and motivate inactive individuals to change their PA behavior is PA-promoting smartphone games. While short-term results of these games are encouraging, the long-term success in effectively changing PA behavior has to date not been confirmed. It is possible that an insufficient incorporation of motivational elements or flaws in gameplay and storyline in these games affect the long-term motivation to play and thereby prevent sustained changes in PA behavior. We aimed to address these design challenges by developing a PA-promoting smartphone game that incorporates established behavior change techniques and specifically targets inactive type 2 diabetes patients. Objective To investigate if a self-developed, behavior change technique-based smartphone game designed by an interdisciplinary team is able to motivate inactive individuals with type 2 diabetes for regular use and thereby increase their intrinsic PA motivation. Methods Thirty-six inactive, overweight type 2 diabetes patients (45-70 years of age) were randomly assigned to either the intervention group or the control group (one-time lifestyle counseling). Participants were instructed to play the smartphone game or to implement the recommendations from the lifestyle counseling autonomously during the 24-week intervention period. Intrinsic PA motivation was assessed with an abridged 12-item version of the Intrinsic Motivation Inventory (IMI) before and after the intervention. In addition, adherence to the game-proposed PA recommendations during the intervention period was assessed in the intervention group via the phone-recorded game usage data. Results Intrinsic PA motivation (IMI total score) increased significantly in the intervention group (+6.4 (SD 4.2; P <.001) points) while it decreased by 1.9 (SD 16.5; P =.623) points in the control group. The adjusted difference between both groups was 8.1 (95% CI 0.9, 15.4; P =.029) points. The subscales “interest/enjoyment” (+2.0 (SD 1.9) points, P <.001) and “perceived competence” (+2.4 (SD 2.4) points, P <.001) likewise increased significantly in the intervention group while they did not change significantly in the control group. The usage data revealed that participants in the intervention group used the game for an average of 131.1 (SD 48.7) minutes of in-game walking and for an average of 15.3 (SD 24.6) minutes of strength training per week. We found a significant positive association between total in-game trainin...

To examine the validity of popular smartphone accelerometer applications and a consumer activity wristband compared to a widely used research accelerometer while assessing the impact of the phone's position on the accuracy of step detection. Twenty volunteers from 2 different age groups (Group A: 18-25 years, n = 10; Group B 45-70 years, n = 10) were equipped with 3 iPhone SE smartphones (placed in pants pocket, shoulder bag, and backpack), 1 Samsung Galaxy S6 Edge (pants pocket), 1 Garmin Vivofit 2 wristband, and 2 ActiGraph wGTX+ devices (worn at wrist and hip) while walking on a treadmill (1.6, 3.2, 4.8, and 6.0 km/h) and completing a walking course. All smartphones included 6 accelerometer applications. Video observation was used as gold standard. Validity was evaluated by comparing each device with the gold standard using mean absolute percentage errors (MAPE). The MAPE of the iPhone SE (all positions) and the Garmin Vivofit was small (<3) for treadmill walking ≥3.2 km/h and for free walking. The Samsung Galaxy and hip-worn ActiGraph showed small MAPE only for treadmill walking at 4.8 and 6.0 km/h and for free walking. The wrist-worn ActiGraph showed high MAPE (17-47) for all walking conditions. The iPhone SE and the Garmin Vivofit 2 are accurate tools for step counting in different age groups and during various walking conditions, even during slow walking. The phone's position does not impact the accuracy of step detection, which substantially improves the versatility for physical activity assessment in clinical and research settings.

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Cardiovascular disease often develops during childhood, but the determinants of vascular health and disease in young children remain unclear. The study aimed to investigate the association of obesity and hypertension, as well as physical fitness with retinal microvascular health and large artery stiffness, in children. In this cross-sectional study, 1171 primary school children (aged 7.2±0.4 years) were screened for central retinal arteriolar equivalent (CRAE) and central retinal venular equivalent (CRVE) diameters, pulse wave velocity (PWV), body mass index, blood pressure (BP), and cardiorespiratory fitness by standardized procedures for children. BP was categorized according to the reference values of the population-based German KiGGS study (Kinder- und Jugendgesundheitssurvey [Children- and Adolescents Health Survey]) and the American Academy of Pediatrics guidelines. Overweight (mean [95% CI]: CRAE, 200.5 [197.9–203.2] µm; CRVE, 231.4 [228.6–234.2] µm; PWV, 4.46 [4.41–4.52] m/s) and obese children (CRAE, 200.5 [196.4–204.7] µm; CRVE, 233.3 [229.0–237.7] µm; PWV, 4.51 [4.43–4.60] m/s) had narrower CRAE, wider CRVE, and higher PWV compared with normal-weight children (CRAE: 203.3 [202.5–204.1] µm, P <0.001; CRVE: 230.1 [229.1–230.9] µm, P =0.07; PWV: 4.33 [4.31–4.35] m/s, P <0.001). Children with high-normal BP (CRAE, 202.5 [200.0–205.0] µm; PWV, 4.44 [4.39–4.49] m/s) and BP in the hypertensive range (CRAE, 198.8 [196.7–201.0] µm; PWV, 4.56 [4.51–4.60] m/s) showed narrower CRAE, as well as higher PWV, compared with normotensive peers (CRAE: 203.7 [202.9–204.6] µm, P <0.001; PWV: 4.30 [4.28–4.32] m/s, P <0.001). With each unit increase of body mass index and systolic BP, CRAE decreased and PWV increased significantly. Children with the highest cardiorespiratory fitness had wider CRAE, narrower CRVE, and lower PWV compared with least fit children. Childhood obesity and hypertension, even at preclinical stages, are associated with microvascular and macrovascular impairments in young children. Primary prevention programs targeting physical activity behavior may have the potential to counteract development of small and large vessel disease early in life. Clinical Trial Registration— URL: http://www.clinicaltrials.gov . Unique identifier: NCT02853747.

Background Wrist-worn accelerometers are increasingly used in epidemiological studies to record physical activity. The accelerometer data are usually only analyzed if the convention for compliant wear time is met (i.e. ≥ 10 h per day) but the algorithms to detect wear time have been developed based on data from hip-worn devices only and have not been tested in a free-living setting. The aim of this study was to validate the automatic wear time detection algorithms of one of the most frequently used devices in a free-living setting. Methods Sixty-eight adults wore one ActiGraph GT3X+ accelerometer on the wrist and one on the hip and additionally recorded wear times for each device separately in a diary. Monitoring phase was during three consecutive days in a free-living setting. Wear time was computed by the algorithms of Troiano and Choi and compared to the diary recordings. Results Mean wear time was over 1420 min per day for both devices on all days. Lin’s concordance correlation coefficient for the wrist-worn wear time was 0.73 (0.60; 0.82) when comparing the diary with Troiano and 0.78 (0.67; 0.86) when comparing the diary with Choi. For hip-worn devices the respective values were 0.23 (0.13; 0.33) for Troiano and 0.92 (0.88; 0.95) for Choi. Mean and standard deviation values for absolute percentage errors for wrist-worn devices were − 1.3 ± 8.1% in Troiano and 0.9 ± 7.7% in Choi. The respective values for hip-worn devices were − 17.5 ± 10% in Troiano and − 0.8 ± 4.6% in Choi. Conclusions Hip worn devices may be preferred due to their higher accuracy in physical activity measurement. Automatic wear-time detection can show high errors in individuals, but on a group level, type I, type II, and total errors are generally low when the Choi algorithm is used. In a real-life setting and participants with a high compliance, the algorithm by Choi is sufficient to distinguish wear time from non-wear time on a group level.