OBJECTIVES:To investigate the characteristics of clinical trials conducted in 5 Asian countries over the past 2 years with a focus on: disease conditions, funding sources and age groups. METHODS: ClinicalTrials.gov was searched for trials initiated after January 1, 2010 in the following countries: Indonesia, Korea, Malaysia, Taiwan and Thailand. The 10 most common conditions and trial sponsors were ranked and the percentage of trials in adults and children was calculated. These figures were compared between countries. RESULTS: During the time period, the following number of trials were identified (number; trials per 1,000,000 population): Taiwan (826; 35.6), Korea (1597; 32.7), Thailand (375; 5.4), Malaysia (132; 4.6) and Indonesia (47; 0.2). The most common trials in all countries were for chronic conditions. Trials on type 2 diabetes were the most common trials in Taiwan, Thailand, Malaysia and Indonesia (range: 3-18%), and were the 3 rd most common in Korea (2%). Breast cancer trials were also common in all 5 countries (range: 1-2%) and non-small cell lung cancer trials were common in 4 countries (range: 1-2%), except Indonesia. Funding for the trials was predominantly non-industry in Taiwan, Thailand and Korea (64%, 61% and 53% respectively), but predominantly industry in Malaysia and Indonesia (72% and 54% respectively). Over 40% of trials in Taiwan were sponsored by local medical institutions. Trials in adults alone were the most common in all countries: Korea (84%), Malaysia (84%), Taiwan (81%), Thailand (74%) and Indonesia (65%). CONCLUSIONS: The number of trials per 1,000,000 population was much higher in Taiwan and Korea than in Thailand, Malaysia and Indonesia. Trials conducted in all these countries, however, show strong similarities in terms of the conditions studied; although there are some differences (e.g. funding sources) between the countries that suggest other factors influence clinical trials in these countries.
Background It is well reported that tracking physical activity can lead to sustained exercise routines, which can decrease disease risk. However, most stop using trackers within a couple months of initial use. The reasons people stop using activity trackers can be varied and personal. Understanding the reasons for discontinued use could lead to greater acceptance of tracking and more regular exercise engagement. Objective The aim of this study was to determine the individualistic reasons for nonengagement with activity trackers. Methods Overweight and obese participants (n=30) were enrolled and allowed to choose an activity tracker of their choice to use for 9 weeks. Questionnaires were administered at the beginning and end of the study to collect data on their technology use, as well as social, physiological, and psychological attributes that may influence tracker use. Closeout interviews were also conducted to further identify individual influencers and attributes. In addition, daily steps were collected from the activity tracker. Results The results of the study indicate that participants typically valued the knowledge of their activity level the activity tracker provided, but it was not a sufficient motivator to overcome personal barriers to maintain or increase exercise engagement. Participants identified as extrinsically motivated were more influenced by wearing an activity tracker than those who were intrinsically motivated. During the study, participants who reported either owning multiple technology devices or knowing someone who used multiple devices were more likely to remain engaged with their activity tracker. Conclusions This study lays the foundation for developing a smart app that could promote individual engagement with activity trackers.
It is well established that lack of physical activity is detrimental to overall health of an individual. Modern day activity trackers enable individuals to monitor their daily activity to meet and maintain targets and to promote activity encouraging behavior.However, the benefits of activity trackers are attenuated over time due to waning adherence. One of the key methods to improve adherence to goals is to motivate individuals to improve on their historic performance metrics. In this work we developed a machine learning model to dynamically adjust the activity target for the forthcoming week that can be realistically achieved by the activity-tracker users. This model prescribes activity target for the forthcoming week. We considered individual userspecific personal, social, and environmental factors, daily step count through the current week (7 days). In addition, we computed an entropy measure that characterizes the pattern of daily step count for the current week. Data for training the machine learning model was collected from 30 participants over a duration of 9 weeks. The model predicted target daily count with mean absolute error of 1545 steps. The proposed work can be used to set personalized goals in accordance with the individual's level of activity and thereby improving adherence to fitness tracker.
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