scholarly journals Use of Connected Technologies to Assess Barriers and Stressors for Age and Disability-Friendly Communities

2021 ◽  
Vol 9 ◽  
Author(s):  
Preeti Zanwar ◽  
Jinwoo Kim ◽  
Jaeyoon Kim ◽  
Michael Manser ◽  
Youngjib Ham ◽  
...  
Keyword(s):  
Physical Activity ◽  
Older Adults ◽  
Electrodermal Activity ◽  
Structural Characteristics ◽  
Brain Activity ◽  
Human Perception ◽  
Machine Learning Algorithms ◽  
Environmental Signals ◽  
Community Environments ◽  
Safety Attributes

Background: The benefits of engaging in outdoor physical activity are numerous for older adults. However, previous work on outdoor monitoring of physical activities did not sufficiently identify how older adults characterize and respond to diverse elements of urban built environments, including structural characteristics, safety attributes, and aesthetics.Objective: To synthesize emerging multidisciplinary trends on the use of connected technologies to assess environmental barriers and stressors among older adults and for persons with disability.Methods: A multidisciplinary overview and literature synthesis.Results: First, we review measurement and monitoring of outdoor physical activity in community environments and during transport using wearable sensing technologies, their contextualization and using smartphone-based applications. We describe physiological responses (e.g., gait patterns, electrodermal activity, brain activity, and heart rate), stressors and physical barriers during outdoor physical activity. Second, we review the use of visual data (e.g., Google street images, Street score) and machine learning algorithms to assess physical (e.g., walkability) and emotional stressors (e.g., stress) in community environments and their impact on human perception. Third, we synthesize the challenges and limitations of using real-time smartphone-based data on driving behavior, incompatibility with software data platforms, and the potential for such data to be confounded by environmental signals in older adults. Lastly, we summarize alternative modes of transport for older adults and for persons with disability.Conclusion: Environmental design for connected technologies, interventions to promote independence and mobility, and to reduce barriers and stressors, likely requires smart connected age and disability-friendly communities and cities.

Frontal Asymmetry, Dispositional Affect, and Physical Activity in Older Adults

1999 ◽  
Vol 7 (1) ◽  
pp. 76-90 ◽  
Author(s):  
Eric E. Hall ◽  
Steven J. Petruzzello
Keyword(s):  
Physical Activity ◽  
Older Adults ◽  
Negative Affect ◽  
Positive Affect ◽  
Satisfaction With Life ◽  
Brain Activity ◽  
Active Group ◽  
Affective Valence ◽  
Frontal Asymmetry ◽  
Activation Patterns

Physical activity has been consistently linked to better mental health—greater positive affect and life satisfaction, less negative affect, anxiety, and depression (Petruzzello et al., 1991; McAuley & Rudolph, 1995). Brain activation patterns have been linked to dispositional affect: greater relative left anterior hemisphere activation relates to positive affect, and greater relative right anterior activation relates to negative affect (Davidson, 1992). In this study, measures of resting EEG frontal asymmetry, dispositional affect, and physical activity were obtained from 41 older adults. Frontal asymmetry significantly predicted positive affect. In the high active group (n = 21), frontal asymmetry significantly predicted affective valence and satisfaction with life; in the low active group (n = 20), it significantly predicted negative affect. Physical activity was also significantly related to better dispositional affect. These findings suggest that the relationship between frontal brain activity and dispositional affect is influenced by physical activity in older adults.

scholarly journals Age and APOE genotype affect the relationship between objectively measured physical activity and power in the alpha band, a marker of brain disease.

2020 ◽  
Author(s):  
Jaisalmer de Frutos-Lucas ◽  
Pablo Cuesta ◽  
Federico Ramirez-Toraño ◽  
Alberto Nebreda ◽  
Esther Cuadrado-Soto ◽  
...  
Keyword(s):  
Physical Activity ◽  
Older Adults ◽  
Protective Factor ◽  
Brain Activity ◽  
Apoe Genotype ◽  
Alpha Power ◽  
Alpha Band ◽  
Electrophysiological Studies ◽  
The Relationship ◽  
Predicted Risk

Abstract BACKGROUND Electrophysiological studies show that reductions in power within the alpha band are associated with the Alzheimer’s disease (AD) continuum. Physical activity (PA) is a protective factor that has proved to reduce AD risk and pathological brain burden. Previous research has confirmed that exercise increases power in the alpha range. However, little is known regarding whether other non-modifiable risk factors for AD, such as increased age or APOE ε4 carriage, alter the association between PA and power in the alpha band.METHODS The relationship between PA and alpha power band was examined in a sample of 113 healthy adults using magnetoencephalography. Additionally, we explored whether ε4 carriage and age modulate this association. The correlations between alpha power and gray matter volumes and cognition were also investigated.RESULTS We detected a parieto-occipital cluster in which PA positively correlated with alpha power. The association between PA and alpha power remained following stratification of the cohort by genotype. Younger and older adults were investigated separately, and younger adults only exhibited a positive relationship between PA and alpha power. Interestingly, when four groups were created based on age (younger-older adult) and APOE (E3/E3-E3/E4), only younger E3/E3 (least predicted risk) and older E3/E4 (greatest predicted risk) had associations between greater alpha power and higher PA. Among older E3/E4, greater alpha power in these regions was associated with improved memory and preserved brain structure.CONCLUSION PA could protect against the slowing of brain activity that characterizes the AD continuum, where it is of benefit for all individuals, especially E3/E4 older adults.

scholarly journals Physical Activity Is Linked to Greater Moment-To-Moment Variability in Spontaneous Brain Activity in Older Adults

PLoS ONE ◽  
2015 ◽  
Vol 10 (8) ◽  
pp. e0134819 ◽  
Author(s):  
Agnieszka Z. Burzynska ◽  
Chelsea N. Wong ◽  
Michelle W. Voss ◽  
Gillian E. Cooke ◽  
Neha P. Gothe ◽  
...  
Keyword(s):  
Physical Activity ◽  
Older Adults ◽  
Brain Activity ◽  
Spontaneous Brain Activity

scholarly journals Age and APOE genotype affect the relationship between objectively measured physical activity and power in the alpha band, a marker of brain disease.

2020 ◽  
Author(s):  
Jaisalmer de Frutos-Lucas ◽  
Pablo Cuesta ◽  
Federico Ramirez-Toraño ◽  
Alberto Nebreda ◽  
Esther Cuadrado-Soto ◽  
...  
Keyword(s):  
Physical Activity ◽  
Older Adults ◽  
Protective Factor ◽  
Brain Activity ◽  
Apoe Genotype ◽  
Alpha Power ◽  
Alpha Band ◽  
Electrophysiological Studies ◽  
The Relationship ◽  
Predicted Risk

Abstract BACKGROUND: Electrophysiological studies show that reductions in power within the alpha band are associated with the Alzheimer’s disease (AD) continuum. Physical activity (PA) is a protective factor that has proved to reduce AD risk and pathological brain burden. Previous research has confirmed that exercise increases power in the alpha range. However, little is known regarding whether other non-modifiable risk factors for AD, such as increased age or APOE ε4 carriage, alter the association between PA and power in the alpha band. METHODS: The relationship between PA and alpha band power was examined in a sample of 113 healthy adults using magnetoencephalography. Additionally, we explored whether ε4 carriage and age modulate this association. The correlations between alpha power and gray matter volumes and cognition were also investigated. RESULTS: We detected a parieto-occipital cluster in which PA positively correlated with alpha power. The association between PA and alpha power remained following stratification of the cohort by genotype. Younger and older adults were investigated separately, and only younger adults exhibited a positive relationship between PA and alpha power. Interestingly, when four groups were created based on age (younger-older adult) and APOE (E3/E3-E3/E4), only younger E3/E3 (least predicted risk) and older E3/E4 (greatest predicted risk) had associations between greater alpha power and higher PA. Among older E3/E4, greater alpha power in these regions was associated with improved memory and preserved brain structure. CONCLUSION: PA could protect against the slowing of brain activity that characterizes the AD continuum, where it is of benefit for all individuals, especially E3/E4 older adults.

scholarly journals Cross-Domain Classification of Physical Activity Intensity: An EDA-Based Approach Validated by Wrist-Measured Acceleration and Physiological Data

Electronics ◽  
2021 ◽  
Vol 10 (17) ◽  
pp. 2159
Author(s):  
Angelica Poli ◽  
Veronica Gabrielli ◽  
Lucio Ciabattoni ◽  
Susanna Spinsante
Keyword(s):  
Physical Activity ◽  
Support Vector Machine ◽  
Electrodermal Activity ◽  
Machine Learning Algorithms ◽  
Physiological Data ◽  
Support Vector ◽  
Physical Activity Intensity ◽  
Activity Data ◽  
Perceived Effort ◽  
Activity Intensity

Performing regular physical activity positively affects individuals’ quality of life in both the short- and long-term and also contributes to the prevention of chronic diseases. However, exerted effort is subjectively perceived from different individuals. Therefore, this work explores an out-of-laboratory approach using a wrist-worn device to classify the perceived intensity of physical effort based on quantitative measured data. First, the exerted intensity is classified by two machine learning algorithms, namely the Support Vector Machine and the Bagged Tree, fed with features computed on heart-related parameters, skin temperature, and wrist acceleration. Then, the outcomes of the classification are exploited to validate the use of the Electrodermal Activity signal alone to rate the perceived effort. The results show that the Support Vector Machine algorithm applied on physiological and acceleration data effectively predicted the relative physical activity intensities, while the Bagged Tree performed best when the Electrodermal Activity data were the only data used.

Smartwatch Step-Counting App for Older Adults: Development and Evaluation Study (Preprint)

2021 ◽  
Author(s):  
George Boateng ◽  
Curtis L. Petersen ◽  
David Kotz ◽  
Karen L. Fortuna ◽  
Rebecca Masutani ◽  
...  
Keyword(s):  
Physical Activity ◽  
Older Adults ◽  
Field Study ◽  
Ground Truth ◽  
Error Rates ◽  
Machine Learning Algorithms ◽  
Free Living ◽  
Step Counting ◽  
Best Parameter ◽  
Counting Algorithm

BACKGROUND Older adults who engage in physical activity can reduce their risk of mobility and disability. Short amounts of walking can improve their quality of life, physical function, and cardiovascular health. Various programs have been implemented to encourage older adults to engage in physical activity, but sustaining their motivation continues to be a challenge. Ubiquitous devices, such as mobile phones and smartwatches, coupled with machine-learning algorithms, can potentially encourage older adults to be more physically active. Current algorithms that are deployed in consumer devices (e.g., Fitbit) are proprietary, often are not tailored to the movements of older adults and have been shown to be inaccurate in clinical settings. Few studies have developed step-counting algorithms for smartwatches – but only using data from younger adults and often validating them only in controlled laboratory settings. OBJECTIVE In this work, we sought to develop and validate a smartwatch step-counting app targeting older adults that has been evaluated in free-living settings over a long period of time (24 weeks) with a large sample (N=42). METHODS the steps of older adults. The app includes algorithms to infer the level of physical activity and to count steps. We validated the step-counting algorithm with a total of 42 older adults in the lab (counting from a video recording, N= 20) and in free-living conditions — one 2-day field study (N=6) and two 12-week field studies (using the Fitbit as ground truth, N=16). During system development, we evaluated four kinds of walking patterns: normal, fast, up and down a staircase, and intermittent speed. For the field study, we evaluated various values for algorithm parameters, and subsequently evaluated the method’s performance using correlations and error rates. RESULTS The results from the evaluation showed that our step-counting algorithm performs well, highly correlated with the ground truth and with low error rate. For the lab study, there was stronger correlation for normal walking R2=0.5; across all activities, the Amulet was on average 3.2 (2.1%) steps lower (SD = 25.9) than video-validated steps. For the 2-day field study, the best parameter settings led to an association between Amulet and Fitbit (R2 of 0.989) and 3.1% (SD=25.1) steps lower than Fitbit, respectively. For the 12-week field study, the best parameter setting led to an R2 of 0.669. CONCLUSIONS Our findings demonstrate the importance of an iterative process in algorithm development in advance of field-based deployment. This work highlights various challenges and insights involved in developing and validating monitoring systems in real-world settings. Nonetheless, our step-counting app for older adults had good performance relative to the ground truth (a commercial Fitbit step-counter). Our app could potentially be used to improve the physical activity among older adults through accurate tracking of their step counts and in-app daily step-count goals.

scholarly journals Age and APOE genotype affect the relationship between objectively measured physical activity and power in the alpha band, a marker of brain disease

2020 ◽  
Vol 12 (1) ◽  
Author(s):  
Jaisalmer de Frutos-Lucas ◽  
Pablo Cuesta ◽  
Federico Ramírez-Toraño ◽  
Alberto Nebreda ◽  
Esther Cuadrado-Soto ◽  
...  
Keyword(s):  
Physical Activity ◽  
Older Adults ◽  
Protective Factor ◽  
Brain Activity ◽  
Apoe Genotype ◽  
Alpha Power ◽  
Alpha Band ◽  
Electrophysiological Studies ◽  
The Relationship ◽  
Predicted Risk

Abstract Background Electrophysiological studies show that reductions in power within the alpha band are associated with the Alzheimer’s disease (AD) continuum. Physical activity (PA) is a protective factor that has proved to reduce AD risk and pathological brain burden. Previous research has confirmed that exercise increases power in the alpha range. However, little is known regarding whether other non-modifiable risk factors for AD, such as increased age or APOE ε4 carriage, alter the association between PA and power in the alpha band. Methods The relationship between PA and alpha band power was examined in a sample of 113 healthy adults using magnetoencephalography. Additionally, we explored whether ε4 carriage and age modulate this association. The correlations between alpha power and gray matter volumes and cognition were also investigated. Results We detected a parieto-occipital cluster in which PA positively correlated with alpha power. The association between PA and alpha power remained following stratification of the cohort by genotype. Younger and older adults were investigated separately, and only younger adults exhibited a positive relationship between PA and alpha power. Interestingly, when four groups were created based on age (younger-older adult) and APOE (E3/E3-E3/E4), only younger E3/E3 (least predicted risk) and older E3/E4 (greatest predicted risk) had associations between greater alpha power and higher PA. Among older E3/E4, greater alpha power in these regions was associated with improved memory and preserved brain structure. Conclusion PA could protect against the slowing of brain activity that characterizes the AD continuum, where it is of benefit for all individuals, especially E3/E4 older adults.

Inefficient Encoding as an Explanation for Age-Related Deficits in Recollection-Based Processing

2014 ◽  
Vol 28 (3) ◽  
pp. 148-161 ◽  
Author(s):  
David Friedman ◽  
Ray Johnson
Keyword(s):  
Older Adults ◽  
Young Adults ◽  
Cognitive Processes ◽  
Brain Activity ◽  
Memory Performance ◽  
Brain Regions ◽  
Semantic Retrieval ◽  
Age Related ◽  
The Face ◽  
Episodic Memories

A cardinal feature of aging is a decline in episodic memory (EM). Nevertheless, there is evidence that some older adults may be able to “compensate” for failures in recollection-based processing by recruiting brain regions and cognitive processes not normally recruited by the young. We review the evidence suggesting that age-related declines in EM performance and recollection-related brain activity (left-parietal EM effect; LPEM) are due to altered processing at encoding. We describe results from our laboratory on differences in encoding- and retrieval-related activity between young and older adults. We then show that, relative to the young, in older adults brain activity at encoding is reduced over a brain region believed to be crucial for successful semantic elaboration in a 400–1,400-ms interval (left inferior prefrontal cortex, LIPFC; Johnson, Nessler, & Friedman, 2013 ; Nessler, Friedman, Johnson, & Bersick, 2007 ; Nessler, Johnson, Bersick, & Friedman, 2006 ). This reduced brain activity is associated with diminished subsequent recognition-memory performance and the LPEM at retrieval. We provide evidence for this premise by demonstrating that disrupting encoding-related processes during this 400–1,400-ms interval in young adults affords causal support for the hypothesis that the reduction over LIPFC during encoding produces the hallmarks of an age-related EM deficit: normal semantic retrieval at encoding, reduced subsequent episodic recognition accuracy, free recall, and the LPEM. Finally, we show that the reduced LPEM in young adults is associated with “additional” brain activity over similar brain areas as those activated when older adults show deficient retrieval. Hence, rather than supporting the compensation hypothesis, these data are more consistent with the scaffolding hypothesis, in which the recruitment of additional cognitive processes is an adaptive response across the life span in the face of momentary increases in task demand due to poorly-encoded episodic memories.

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