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.
PurposeThe spread of COVID-19 and the associated stay-at-home orders and shutdowns of gyms and fitness centers have drastically influenced health behaviors leading to widespread reductions in physical activity (PA). The recent Call to Action from the American College of Sports Medicine has promoted “innovative strategies to promote PA during the COVID-19 pandemic.” We aimed to identify individual-level factors that protected against declines in PA levels amid the COVID-19 restrictions.MethodsWe used the Pennington Biomedical COVID-19 Health Behaviors Survey for our analyses and used mixed-effect linear and generalized linear models to estimate the effects of individual-level factors on changes in PA levels during the COVID-19 restrictions.ResultsParticipants (n = 4376) provided information on PA behaviors before and during the COVID-19 shutdown. Overall, PA levels declined by a mean ± SD of 112 ± 1460 MET·min·wk−1 during the COVID-19 shutdown; however, changes in PA were heterogeneous, with 55% of the participants reporting increases in or maintenance of PA during that time. Several social and demographic factors were significantly related to declines in PA, including high prepandemic PA levels, living alone (difference = 118 MET·min·wk−1), low household income (difference between the highest and the lowest income group = 363 MET·min·wk−1), COVID-19-related changes in income (difference = 110 MET·min·wk−1), and loss of employment (difference = 168 MET·min·wk−1). The substitution of prepandemic gym attendance with the purchase and use of home exercise equipment or exercise through virtual fitness platforms promoted increases in PA during the COVID-19 shutdown.ConclusionsWhile promoting PA through the COVID-19 pandemic, it is important to consider demographic factors, which greatly influence health behaviors and implementation of, and access to, replacement behaviors. The promotion of such strategies could help maintain PA levels during potential future stay-at-home orders.
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