Using continuous sensor data to formalize a model of in-home activity patterns

Formal modeling and analysis of human behavior can properly advance disciplines ranging from psychology to economics. The ability to perform such modeling has been limited by a lack of ecologically-valid data collected regarding human daily activity. We propose a formal model of indoor routine behavior based on data from automatically-sensed and recognized activities. A mechanistic description of behavior patterns for identical activity is offered to both investigate behavioral norms with 99 smart homes and compare these norms between subgroups. We identify and model the patterns of human behaviors based on inter-arrival times, the time interval between two successive activities, for selected activity classes in the smart home dataset with diverse participants. We also explore the inter-arrival times of sequence of activities in one smart home. To demonstrate the impact such analysis can have on other disciplines, we use this same smart home data to examine the relationship between the formal model and resident health status. Our study reveals that human indoor activities can be described by non-Poisson processes and that the corresponding distribution of activity inter-arrival times follows a Pareto distribution. We further discover that the combination of activities in certain subgroups can be described by multivariate Pareto distributions. These findings will help researchers understand indoor activity routine patterns and develop more sophisticated models of predicting routine behaviors and their timings. Eventually, the findings may also be used to automate diagnoses and design customized behavioral interventions by providing activity-anticipatory services that will benefit both caregivers and patients

Automated smart home assessment to support pain management: Multiple methods analysis

©Roschelle L Fritz, Marian Wilson, Gordana Dermody, Maureen Schmitter-Edgecombe, Diane J Cook. Objective: This study aimed to determine if a smart home can detect pain-related behaviors to perform automated assessment and support intervention for persons with chronic pain.Background: Poorly managed pain can lead to substance use disorders, depression, suicide, worsening health, and increased use of health services. Most pain assessments occur in clinical settings away from patients’ natural environments. Advances in smart home technology may allow observation of pain in the home setting. Smart homes recognizing human behaviors may be useful for quantifying functional pain interference, thereby creating new ways of assessing pain and supporting people living with pain.Methods: A multiple methods, secondary data analysis was conducted using historic ambient sensor data and weekly nursing assessment data from 11 independent older adults reporting pain across 1-2 years of smart home monitoring. A qualitative approach was used to interpret sensor-based data of 27 unique pain events to support clinician-guided training of a machine learning model. A periodogram was used to calculate circadian rhythm strength, and a random forest containing 100 trees was employed to train a machine learning model to recognize pain-related behaviors. The model extracted 550 behavioral markers for each sensor-based data segment. These were treated as both a binary classification problem (event, control) and a regression problem.Results: We found 13 clinically relevant behaviors, revealing 6 pain-related behavioral qualitative themes. Quantitative results were classified using a clinician-guided random forest technique that yielded a classification accuracy of 0.70, sensitivity of 0.72, specificity of 0.69, area under the receiver operating characteristic curve of 0.756, and area under the precision-recall curve of 0.777 in comparison to using standard anomaly detection techniques without clinician guidance (0.16 accuracy achieved; P \u3c .001). The regression formulation achieved moderate correlation, with r=0.42.Conclusions: Findings of this secondary data analysis reveal that a pain-assessing smart home may recognize pain-related behaviors. Utilizing clinicians’ real-world knowledge when developing pain-assessing machine learning models improves the model’s performance. A larger study focusing on pain-related behaviors is warranted to improve and test model performance

Cost effectiveness of a cultural physical activity intervention to reduce blood pressure among Native Hawaiians with hypertension

Objective: The aim of this study was to calculate the costs and assess whether a culturally grounded physical activity intervention offered through community-based organizations is cost effective in reducing blood pressure among Native Hawaiian adults with hypertension. Methods: Six community-based organizations in Hawai'i completed a randomized controlled trial between 2015 and 2019. Overall, 263 Native Hawaiian adults with uncontrolled hypertension (≥ 140 mmHg systolic, ≥ 90 mmHg diastolic) were randomized to either a 12-month intervention group of hula (traditional Hawaiian dance) lessons and self-regulation classes, or to an education-only waitlist control group. The primary outcome was change in systolic blood pressure collected at baseline and 3, 6, and 12 months for the intervention compared with the control group. Incremental cost-effectiveness ratios (ICERs) were calculated for primary and secondary outcomes. Non-parametric bootstrapping and sensitivity analyses evaluated uncertainty in parameters and outcomes. Results: The mean intervention cost was US$361/person, and the 6-month ICER was US$103/mmHg reduction in systolic blood pressure and US$95/mmHg in diastolic blood pressure. At 12 months, the intervention group maintained reductions in blood pressure, which exceeded reductions for usual care based on blood pressure outcomes. The change in blood pressure at 12 months resulted in ICERs of US$100/mmHg reduction in systolic blood pressure and US$93/mmHg in diastolic blood pressure. Sensitivity analyses suggested that at the estimated intervention cost, the probability that the program would lower systolic blood pressure by 5 mmHg was 67 and 2.5% at 6 and 12 months, respectively. Conclusion: The 6-month Ola Hou program may be cost effective for low-resource community-based organizations. Maintenance of blood pressure reductions at 6 and 12 months in the intervention group contributed to potential cost effectiveness. Future studies should further evaluate the cost effectiveness of indigenous physical activity programs in similar settings and by modeling lifetime costs and quality-adjusted life-years. Trial registration number: NCT02620709.Sociolog

Costs of a predictable switch between simple cognitive tasks following severe closed-head injury

The authors used a predictable, externally cued task-switching paradigm to investigate executive control in a severe closed-head injury (CHI) population. Eighteen individuals with severe CHI and 18 controls switched between classifying whether a digit was odd or even and whether a letter was a consonant or vowel on every 4th trial. The target stimuli appeared in a circle divided into 8 equivalent parts. Presentation of the stimuli rotated clockwise. Participants performed the switching task at both a short (200 ms) and a long (1,000 ms) preparatory interval. Although the participants with CHI exhibited slower response times and greater switch costs, similar to controls, additional preparatory time reduced the switch costs, and the switch costs were limited to the 1st trial in the run. These findings indicate that participants with severe CHI were able to take advantage of time to prepare for the task switch, and the executive control processes involved in the switch costs were completed before the 1st trial of the run ended

Naturalistic assessment of executive function and everyday multitasking in healthy older adults

Everyday multitasking and its cognitive correlates were investigated in an older adult population using a naturalistic task, the Day Out Task. Fifty older adults and 50 younger adults prioritized, organized, initiated, and completed a number of subtasks in a campus apartment to prepare for a day out (e.g., gather ingredients for a recipe, collect change for a bus ride). Participants also completed tests assessing cognitive constructs important in multitasking. Compared to younger adults, the older adults took longer to complete the everyday tasks and more poorly sequenced the subtasks. Although they initiated, completed, and interweaved a similar number of subtasks, the older adults demonstrated poorer task quality and accuracy, completing more subtasks inefficiently. For the older adults, reduced prospective memory abilities were predictive of poorer task sequencing, while executive processes and prospective memory were predictive of inefficiently completed subtasks. The findings suggest that executive dysfunction and prospective memory difficulties may contribute to the age-related decline of everyday multitasking abilities in healthy older adults

Feeling-of-Knowing in Episodic Memory following Moderate-to-Severe Closed-head Injury

The ability to accurately monitor one’s memory is a metacognitive process that is important in everyday life. In this study, we examined episodic memory feeling-of-knowing (FOK) ratings in 21 moderate-to-severe closed-head injury (CHI) participants (> 1 year post injury) and 21 controls. Participants studied 36 critical cue-target word pairs. Following a brief delay, they were asked to recall the target that corresponded to a given cue. Confidence ratings were made for recalled words and FOK judgments were made for non-recalled words in terms of the likelihood of recognizing the target word on a subsequent recognition test. We found that CHI participants demonstrated less accurate recall but accurate ability to judge their recall performance (retrospective memory monitoring). CHI participants also demonstrated intact feeling-of-knowing judgments when providing binary judgments, but demonstrated difficulties making finer discriminations on an ordinal scale (prospective memory monitoring). These findings suggest that memory monitoring is not a unitary construct. It is proposed that CHI participants may display intact memory monitoring when predictions are based on familiarity assessment but not when continued probing for additional episodic information is required