Background: Agitation and aggression are common in patients with Alzheimer’s disease and related dementias and pose a significant burden on patients, caregivers, and the healthcare systems. Guidelines recommend personalized behavioral interventions as the first-line treatment; however, these interventions are often underutilized. The Standardizing Care for Neuropsychiatric Symptoms and Quality of Life in Dementia (StaN) study (ClinicalTrials.gov Identifier # NCT0367220) is a multisite randomized controlled trial comparing an Integrated Care Pathway, that includes a sequential pharmacological algorithm and structured behavioral interventions, with treatment-as-usual to treat agitation in dementia in long-term care and inpatient settings. Objective: To describe the rationale and design of structured behavioral interventions in the StaN study. Methods: Structured behavioral interventions are designed and implemented based on the following considerations: 1) personalization, 2) evidence base, 3) dose and duration, 4) measurement-based care, and 5) environmental factors and feasibility. Results: The process to design behavioral interventions for each individual starts with a comprehensive assessment, followed by personalized, evidence-based interventions delivered in a standardized manner with ongoing monitoring of global clinical status. Measurement-based care is used to tailor the interventions and to integrate them with pharmacotherapy. Conclusion: Individualized behavioral interventions in patients with dementia may be challenging to design and implement. Here we describe a process to design and implement individualized and structured behavioral interventions in the context of a multisite trial in long-term care and inpatient settings. This process can inform the design of behavioral interventions in future trials and in clinical settings for the treatment of agitation in dementia.

People use how-to videos, both live and pre-recorded, to learn new physical skills [37], ranging from repairing a broken keyboard to learning digital fabrication [42]. In most how-to videos for physical skills, the instructor demonstrates step-by-step how to complete the task [14]. As these steps may involve activities at varying locations in varying levels of detail, a single, fixed camera often cannot record every step with the desired clarity [39]. This necessitates frequent changes to camera parameters, including viewpoints, angles, and zoom levels.Professional video productions, such as cooking and home improvement shows, employ several dedicated camera operators who actively re-position cameras and adjust their parameters in response to the instructor's actions. However, such resources are not available to most instructors; instead, these rely on one or more preconfigured fixed cameras. Although fixed camera setups can be re-configured during recording, instructors need to stop what they are demonstrating (e.g., chopping vegetables) to manipulate the camera. This disrupts the demonstration, increasing the instructor's workload. It also requires more post-processing to combine clips filmed with different camera setups.Both filmmakers and researchers have explored the idea of cameramanipulating robots as an alternative to human operators [1,27]. Recent camera robots (predominantly drones) can autonomously track moving subjects [6,24,41]. However, it remains a challenge for the user being filmed to control the camera robots' behaviors while performing other activities, such as demonstrating a physical process. Conventional interfaces for robot control employ joysticks [52], gestures [49], and speech [16]-all of which require dedicated input actions-that disrupt instruction delivery. If not edited out, such disruptions might split audience's attention and hinder learning [9], but post-processing adds to instructors' efforts. Recent user interface research has explored triggering on-screen visual effects through presenters' gestures and speech [22,32,48] that are part of the presentations. Our approach in this work is to:(1) identify the kinds of camera shots that how-to videos use and (2) direct camera operations in a non-disruptive manner by relying on the communicative signals that instructors already use to address their audience during demonstrations. For instance, an instructor may point to a part of an object to emphasize it, use speech to guide the audience's attention, or wave to introduce themselves.

Background Polypharmacy is common in patients with dementia and is associated with several adverse effects. Factors associated with polypharmacy in patients with behavioral and psychological symptoms of dementia (BPSD) are unclear. This study examined the factors associated with general and psychotropic polypharmacy in patients with BPSD. Method Baseline data was obtained from the Standardizing Care for Neuropsychiatric Symptoms and Quality of Life in Dementia (StaN) study, a multisite trial currently underway in Canada at long‐term care and inpatient sites. The Cohen‐Mansfield Agitation Inventory (CMAI) was used to assess agitation and aggression and Cumulative Illness Rating Scale for Geriatrics (CIRS‐G) was used to assess medical burden. General polypharmacy was defined as concomitant use of five or more scheduled medications of any kind, and psychotropic polypharmacy was defined as concomitant use of two or more scheduled psychotropic medications. Correlation, and linear and logistic regressions were performed to investigate the associations of agitation/aggression and polypharmacy. Result 120 participants were enrolled [50.4% female; mean (SD) age= 80.25 (9.75) years]. 80.2% of participants had general polypharmacy and 56.2% had psychotropic polypharmacy. Total number of medications was positively correlated with CIRS_G (Spearman's r = 0.28, p = 0.002). In linear regression models, CMAI‐frequency (R2 = 0.137, p = 0.003, B = 0.269, p = 0.003) and CMAI‐disruptiveness (R2 = 0.136, p = 0.003, B = 0.268, p = 0.004) were significant predictors, whereas age, current psychiatric diagnosis and setting (inpatient versus long‐term) did not predict the number of psychotropics. Similarly in logistic regression models, CMAI‐frequency (R2 = 0.142, p = 0.002, Wald = 6.93, p = 0.008) and CMAI‐disruptiveness (R2 = 0.141, p = 0.002, Wald = 6.86, p = 0.009) were significant predictors, whereas age, current psychiatric diagnosis and setting did not predict psychotropic polypharmacy. Conclusion As expected, medical burden was positively correlated with general medication use. On the other hand, BPSD (agitation and aggression) were associated with total number of psychotropic medications, rather than psychiatric morbidity, age or setting. Improving the management of agitation/aggression might be an important factor in addressing polypharmacy in patients with BPSD.