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Background: Getting lost is one of the earliest and most distressing symptoms seen in Alzheimer's disease (AD). Despite being a prevalent problem in the community worldwide, very few studies have explored real‐world environmental factors that may potentially contribute to patients getting lost. In this study, we aim to investigate whether road network structure plays a contributory role to AD patients getting lost in the community using retrospective and prospective data. Method: Retrospective data of police case records of a large sample of missing dementia patients in the Norfolk county (n = 210), over a three year period, was used first to test our aim. Here, for each missing patient location, we measured the road intersection density, intersection complexity, as well as orientation entropy at a 1 km radius buffer zone around these locations; these measures were then compared to that of a set of random but matched locations. We then collected data prospectively to test the performance of 18 community‐dwelling patients with AD (aged 50‐80 years) on their ability to find their way in their own neighbourhood using a novel 'Detour Navigation Task'. Here, we will measure the road intersection density, complexity, and orientation entropy at the locations in their neighbourhood where patients exhibited spatial disorientation. Result: The 210 patients in the police data went missing from a total of 168 locations in the Norfolk county. Our results show that the locations patients went missing from had significantly increased road intersection density, complexity, and orientation entropy when compared to that of the matched locations. Meanwhile, the performance of the sample of 18 AD patients on the Detour Navigation Task will then be analysed to see whether the neighbourhood locations where the patients felt disoriented exhibited higher road intersection density, complexity, and orientation entropy when compared to locations where they did not feel disoriented. Conclusion: The results of this study will provide evidence for road network structure as being a significant factor contributing to AD patients getting lost. This can in future help to potentially identify patients at high risk for getting lost as well as inform safeguarding guidelines. [ABSTRACT FROM AUTHOR]

Background: Getting lost is one of the earliest and most distressing symptoms seen in Alzheimer's disease (AD). Despite being a prevalent problem in the community worldwide, very few studies have explored real‐world environmental factors that may potentially contribute to patients getting lost. In this study, we aim to investigate whether road network structure plays a contributory role to AD patients getting lost in the community using retrospective and prospective data. Method: Retrospective data of police case records of a large sample of missing dementia patients in the Norfolk county (n = 210), over a three year period, was used first to test our aim. Here, for each missing patient location, we measured the road intersection density, intersection complexity, as well as orientation entropy at a 1 km radius buffer zone around these locations; these measures were then compared to that of a set of random but matched locations. We then collected data prospectively to test the performance of 18 community‐dwelling patients with AD (aged 50‐80 years) on their ability to find their way in their own neighbourhood using a novel 'Detour Navigation Task'. Here, we will measure the road intersection density, complexity, and orientation entropy at the locations in their neighbourhood where patients exhibited spatial disorientation. Result: The 210 patients in the police data went missing from a total of 168 locations in the Norfolk county. Our results show that the locations patients went missing from had significantly increased road intersection density, complexity, and orientation entropy when compared to that of the matched locations. Meanwhile, the performance of the sample of 18 AD patients on the Detour Navigation Task will then be analysed to see whether the neighbourhood locations where the patients felt disoriented exhibited higher road intersection density, complexity, and orientation entropy when compared to locations where they did not feel disoriented. Conclusion: The results of this study will provide evidence for road network structure as being a significant factor contributing to AD patients getting lost. This can in future help to potentially identify patients at high risk for getting lost as well as inform safeguarding guidelines. [ABSTRACT FROM AUTHOR]

Background: Chronic pain is a globally prevalent condition. It is closely linked with psychological well-being, and it is often concomitant with anxiety, negative affect, and in some cases even depressive disorders. In the case of musculoskeletal chronic pain, frequent physical activity is beneficial. However, reluctance to engage in physical activity is common due to negative psychological associations (eg, fear) between movement and pain. It is known that encouragement, self-efficacy, and positive beliefs are effective to bolster physical activity. However, given that the majority of time is spent away from personnel who can give such encouragement, there is a great need for an automated ubiquitous solution.Objective: MyBehaviorCBP is a mobile phone app that uses machine learning on sensor-based and self-reported physical activity data to find routine behaviors and automatically generate physical activity recommendations that are similar to existing behaviors. Since the recommendations are based on routine behavior, they are likely to be perceived as familiar and therefore likely to be actualized even in the presence of negative beliefs. In this paper, we report the preliminary efficacy of MyBehaviorCBP based on a pilot trial on individuals with chronic back pain.Methods: A 5-week pilot study was conducted on people with chronic back pain (N=10). After a week long baseline period with no recommendations, participants received generic recommendations from an expert for 2 weeks, which served as the control condition. Then, in the next 2 weeks, MyBehaviorCBP recommendations were issued. An exit survey was conducted to compare acceptance toward the different forms of recommendations and map out future improvement opportunities.Results: In all, 90% (9/10) of participants felt positive about trying the MyBehaviorCBP recommendations, and no participant found the recommendations unhelpful. Several significant differences were observed in other outcome measures. Participants found MyBehaviorCBP recommendations easier to adopt compared to the control (βint=0.42, P<.001) on a 5-point Likert scale. The MyBehaviorCBP recommendations were actualized more (βint=0.46, P<.001) with an increase in approximately 5 minutes of further walking per day (βint=4.9 minutes, P=.02) compared to the control. For future improvement opportunities, participants wanted push notifications and adaptation for weather, pain level, or weekend/weekday.Conclusions: In the pilot study, MyBehaviorCBP's automated approach was found to have positive effects. Specifically, the recommendations were actualized more, and perceived to be easier to follow. To the best of our knowledge, this is the first time an automated approach has achieved preliminary success to promote physical activity in a chronic pain context. Further studies are needed to examine MyBehaviorCBP's efficacy on a larger cohort and over a longer period of time. [ABSTRACT FROM AUTHOR]