Increased walking among facility-dwelling older adults, who are very old, frail, and have low physical activity, could have substantial health benefits. Multilevel approaches to improving physical activity, based on Ecological Models and Social Cognitive Theory, have not been tested in this population but hold promise for improved effects. This study aimed to investigate the feasibility and outcomes of a 3-month enhanced, multilevel walking intervention, compared to a standard walking intervention, among older adults in retirement communities. Participants in the enhanced intervention group were hypothesized to have improved outcomes compared to those in the standard intervention. Data were collected at baseline (N = 87) and post-intervention (N = 67) from residents in 4 retirement facilities. Sites were quasi- randomized to condition (N = 2 sites per condition). Standard intervention components included pedometers, printed materials, and biweekly group sessions; those in the enhanced intervention also received individual biweekly phone counseling and environmental awareness components. Measures included activity related outcomes (pedometers, sedentary behavior, activities of daily living, on and off-site walking, satisfaction with walking opportunities, neighborhood barriers), physical function, mental health outcomes (quality of life, depression), study satisfaction, and adherence to study components. Data were analyzed using analysis of covariance (ANCOVA) for between group differences and repeated measures ANCOVA for pre-post test changes. None of the outcomes were significantly different between walking intervention conditions except for neighborhood barriers. Standard intervention participants had significantly fewer neighborhood barriers post-intervention compared to the enhanced intervention group. Significant improvements from baseline to post-intervention occurred among the total sample for step counts, neighborhood barriers, walking up stairs, walking off-site, and satisfaction with walking opportunities but significance disappeared after adjustment for covariates. Study satisfaction and adherence was high for both groups. The results of this study suggest that two different types of walking interventions are feasible to conduct and result in improved step counts among facility-dwelling older adults. The most change occurred for environment-related variables. Findings suggest that the context of walking is important for older adults residing in retirement facilities and should be targeted in future interventions. Future studies can build on this novel multilevel approach to improving walking among very old adults

Purpose of the study

The purpose of this study was to describe prevalence of technology use among adults ages 65 and older, particularly for those with disability and activity-limiting symptoms and impairments.

Design and methods

Data from the 2011 National Health and Aging Trends Study, a nationally representative sample of community-dwelling Medicare beneficiaries (N = 7,609), were analyzed. Analysis consisted of technology use (use of e-mail/text messages and the internet) by sociodemographic and health characteristics and prevalence ratios for technology usage by disability status.

Results

Forty percent of older adults used e-mail or text messaging and 42.7% used the internet. Higher prevalence of technology use was associated with younger age, male sex, white race, higher education level, and being married (all p values <.001). After adjustment for sociodemographic and health characteristics, technology use decreased significantly with greater limitations in physical capacity and greater disability. Vision impairment and memory limitations were also associated with lower likelihood of technology use.

Implications

Technology usage in U.S. older adults varied significantly by sociodemographic and health status. Prevalence of technology use differed by the type of disability and activity-limiting impairments. The internet, e-mail, and text messaging might be viable mediums for health promotion and communication, particularly for younger cohorts of older adults and those with certain types of impairment and less severe disability.

Objective

The authors investigated if the physical activity increases observed in the Multilevel Intervention for Physical Activity in Retirement Communities (MIPARC) improved cognitive functions in older adults. The authors also examined if within-person changes in moderate to vigorous physical activity (MVPA), as opposed to low-light and high-light physical activity, were related to cognitive improvements in the entire sample.

Methods

This was a cluster randomized control trial set in retirement communities in San Diego County, CA. A total of 307 older adults without a formal diagnosis of dementia (mean age: 83 years; age range: 67-100; standard deviation: 6.4 years; 72% women) were assigned to the physical activity (N = 151) or healthy education control (N = 156) groups. Interventions were led by study staff for the first 6 months and sustained by peer leaders for the next 6 months. Components included individual counseling and self-monitoring with pedometers, group education sessions, and printed materials. Measurements occurred at baseline, 6 months, and 12 months. Triaxial accelerometers measured physical activity for 1 week. The Trail Making Test (TMT) Parts A and B and a Symbol Search Test measured cognitive functions.

Results

There were no significant differences in cognitive functions between the MIPARC intervention and control groups at 6 or 12 months. Within-person increases in MVPA, and not low-light or high-light physical activity, were associated with improvements in TMT Parts B, B-A, and Symbol Search scores in the entire sample.

Conclusion

Findings suggest that MVPA may have a stronger impact on cognitive functions than lower intensity physical activity within retirement community samples of highly educated older adults without dementia.

This study employed novel GPS methods to assess the effect of a multilevel physical activity (PA) intervention on device-measured walking locations in 305 community dwelling older adults, ages 65+ (mean age = 83, 73% women). Retirement communities were randomized to a 1-year PA intervention that encouraged neighborhood walking, or to a healthy aging control condition. Total time and time spent walking in four life-space domains were assessed using GPS and accelerometer devices. The intervention increased the time spent walking as a proportion of total time spent in the Campus, Neighborhood and Beyond Neighborhood domains. Intervention effects on walking location were observed in both genders and across physical and cognitive functioning groups. Results demonstrate that an intervention providing individual, social and environmental support for walking can increase PA in larger life-space domains for a broad spectrum of older adults.

Abstract Background Sedentary behaviors such as TV viewing are associated with childhood obesity, while physical activity promotes healthy weight. The role of the home environment in shaping these behaviors among youth is poorly understood. The study purpose was to examine the reliability of brief parental proxy-report and adolescent self-report measures of electronic equipment and physical activity equipment in the home and to assess the construct validity of these scales by examining their relationship to physical activity, sedentary behavior, and weight status of children and adolescents. Methods Participants were adolescents (n = 189; mean age = 14.6), parents of adolescents (n = 171; mean age = 45.0), and parents of younger children (n = 116; parents mean age = 39.6; children's mean age = 8.3) who completed two surveys approximately one month apart. Measures included a 21-item electronic equipment scale (to assess sedentary behavior facilitators in the home, in the child or adolescent's bedroom, and portable electronics) and a 14-item home physical activity equipment scale. Home environment factors were examined as correlates of children's and adolescents' physical activity, sedentary behavior, and weight status after adjusting for child age, sex, race/ethnicity, household income, and number of children in the home. Results Most scales had acceptable test-retest reliability (intraclass correlations were .54 - .92). Parent and adolescent reports were correlated. Electronic equipment in adolescents' bedrooms was positively related to sedentary behavior. Activity equipment in the home was inversely associated with television time in adolescents and children, and positively correlated with adolescents' physical activity. Children's BMI z-score was positively associated with having a television in their bedroom. Conclusions The measures of home electronic equipment and activity equipment were similarly reliable when reported by parents and by adolescents. Home environment attributes were related to multiple obesity-related behaviors and to child weight status, supporting the construct validity of these scales.

Background

Machine learning has been used for classification of physical behavior bouts from hip-worn accelerometers; however, this research has been limited due to the challenges of directly observing and coding human behavior "in the wild." Deep learning algorithms, such as convolutional neural networks (CNNs), may offer better representation of data than other machine learning algorithms without the need for engineered features and may be better suited to dealing with free-living data. The purpose of this study was to develop a modeling pipeline for evaluation of a CNN model on a free-living data set and compare CNN inputs and results with the commonly used machine learning random forest and logistic regression algorithms.

Method

Twenty-eight free-living women wore an ActiGraph GT3X+accelerometer on their right hip for 7 days. A concurrently worn thigh-mounted activPAL device captured ground truth activity labels. The authors evaluated logistic regression, random forest, and CNN models for classifying sitting, standing, and stepping bouts. The authors also assessed the benefit of performing feature engineering for this task.

Results

The CNN classifier performed best (average balanced accuracy for bout classification of sitting, standing, and stepping was 84%) compared with the other methods (56% for logistic regression and 76% for random forest), even without performing any feature engineering.

Conclusion

Using the recent advancements in deep neural networks, the authors showed that a CNN model can outperform other methods even without feature engineering. This has important implications for both the model's ability to deal with the complexity of free-living data and its potential transferability to new populations.

Background

Many older adults spend the majority of their waking hours sitting, which increases their risk of chronic diseases. Given the challenges that many older adults face when engaging in moderate-to-vigorous physical activity, understanding the health benefits of decreasing sitting time and increasing the number of sit-to-stand transitions is needed to address this growing public health concern.

Objective

The aim of this 3-arm randomized controlled trial is to investigate how changes in sitting time and brief sit-to-stand transitions impact biomarkers of healthy aging and physical, emotional, and cognitive functioning compared with a healthy attention control arm.

Methods

Sedentary and postmenopausal women (N=405) will be recruited and randomly assigned to 1 of the 3 study conditions for 3 months: healthy living attention control (Healthy Living), reduce sitting time (Reduce Sitting), and increase sit-to-stand transitions (Increase Transitions). Assessments conducted at baseline and 3 months included fasting blood draw, blood pressure, anthropometric measurements, physical functioning, cognitive testing, and 7 days of a thigh-worn accelerometer (activPAL) and a hip-worn accelerometer (ActiGraph). Blood-based biomarkers of healthy aging included those associated with glycemic control (glycated hemoglobin, fasting plasma insulin and glucose, and homeostatic model assessment of insulin resistance).

Results

Recruitment began in May 2018. The intervention is ongoing, with data collection expected to continue through the end of 2022.

Conclusions

The Rise for Health study is designed to test whether 2 different approaches to interrupting sitting time can improve healthy aging in postmenopausal women. Results from this study may inform the development of sedentary behavior guidelines and interventions to reduce sitting time in older adults.

Trial registration

ClinicalTrials.gov NCT03473145; https://clinicaltrials.gov/ct2/show/NCT03473145.

International registered report identifier (irrid)

DERR1-10.2196/28684.

Introduction

Sitting patterns predict several healthy aging outcomes. These patterns can potentially be measured using hip-worn accelerometers, but current methods are limited by an inability to detect postural transitions. To overcome these limitations, we developed the Convolutional Neural Network Hip Accelerometer Posture (CHAP) classification method.

Methods

CHAP was developed on 709 older adults who wore an ActiGraph GT3X+ accelerometer on the hip, with ground-truth sit/stand labels derived from concurrently worn thigh-worn activPAL inclinometers for up to 7 d. The CHAP method was compared with traditional cut-point methods of sitting pattern classification as well as a previous machine-learned algorithm (two-level behavior classification).

Results

For minute-level sitting versus nonsitting classification, CHAP performed better (93% agreement with activPAL) than did other methods (74%-83% agreement). CHAP also outperformed other methods in its sensitivity to detecting sit-to-stand transitions: cut-point (73%), TLBC (26%), and CHAP (83%). CHAP's positive predictive value of capturing sit-to-stand transitions was also superior to other methods: cut-point (30%), TLBC (71%), and CHAP (83%). Day-level sitting pattern metrics, such as mean sitting bout duration, derived from CHAP did not differ significantly from activPAL, whereas other methods did: activPAL (15.4 min of mean sitting bout duration), CHAP (15.7 min), cut-point (9.4 min), and TLBC (49.4 min).

Conclusion

CHAP was the most accurate method for classifying sit-to-stand transitions and sitting patterns from free-living hip-worn accelerometer data in older adults. This promotes enhanced analysis of older adult movement data, resulting in more accurate measures of sitting patterns and opening the door for large-scale cohort studies into the effects of sitting patterns on healthy aging outcomes.

Background

As the US population ages, there is an increasing need for evidence based, peer-led physical activity programs, particularly in ethnically diverse, low income senior centers where access is limited.

Methods/design

The Peer Empowerment Program 4 Physical Activity' (PEP4PA) is a hybrid Type II implementation-effectiveness trial that is a peer-led physical activity (PA) intervention based on the ecological model of behavior change. The initial phase is a cluster randomized control trial randomized to either a peer-led PA intervention or usual center programming. After 18 months, the intervention sites are further randomized to continued support or no support for another 6 months. This study will be conducted at twelve senior centers in San Diego County in low income, diverse communities. In the intervention sites, 24 peer health coaches and 408 adults, aged 50 years and older, are invited to participate. Peer health coaches receive training and support and utilize a tablet computer for delivery and tracking. There are several levels of intervention. Individual components include pedometers, step goals, counseling, and feedback charts. Interpersonal components include group walks, group sharing and health tips, and monthly celebrations. Community components include review of PA resources, walkability audit, sustainability plan, and streetscape improvements. The primary outcome of interest is intensity and location of PA minutes per day, measured every 6 months by wrist and hip accelerometers and GPS devices. Secondary outcomes include blood pressure, physical, cognitive, and emotional functioning. Implementation measures include appropriateness & acceptability (perceived and actual fit), adoption & penetration (reach), fidelity (quantity & quality of intervention delivered), acceptability (satisfaction), costs, and sustainability.

Discussion

Using a peer led implementation strategy to deliver a multi-level community based PA program can enhance program adoption, implementation, and sustainment.

Trial registration

ClinicalTrials.gov, USA ( NCT02405325 ). Date of registration, March 20, 2015. This website also contains all items from the World Health Organization Trial Registration Data Set.