GPS and Smartphone Technology for Real-World Measurement of Community Mobility in Healthcare

Physical activity (PA) enhancement and mental distress reduction are important issues in cancer survivorship care. Mobile technology, as an emerging method for changing health behaviors, is gaining attention from many researchers. This study aimed to investigate the effect of a mobile app-based community on enhancing PA and decreasing distress in breast cancer survivors. We conducted a non-randomized, prospective, interventional study that had a mobile community-later arm and mobile community-first arm. With an Android smartphone app (WalkON®), daily walk steps and weekly distress scores using app-based Distress Thermometer (DT) questionnaires were collected from participants for about 12 weeks. To examine the difference in weekly step counts before and during the community activity, we used a paired t-test method. For a comparative analysis, we referred to a previous prospective observational study without a mobile community intervention that had the same setting as the present study. After propensity score matching (PSM), multivariable regression modeling with difference-in-difference (DID) was performed to estimate the effect of the mobile app-based community on PA and mental distress. From January to August 2018, a total of 64 participants were enrolled in this study. In the univariate analysis, after participation in the mobile community, the participants showed a significant increase in total weekly steps (t = −3.5341; P = 0.00208). The mean of the differences was 10,408.72 steps. In the multivariate analysis after PSM, the mobile community significantly increased steps by 8,683.4 per week (p value <0.0001) and decreased DT scores by 0.77 per week (p value = 0.009) in the mixed effect model. In the two-way fixed effect model, the mobile community showed a significant increase in weekly steps by 8,723.4 (p value <0.0001) and decrease in weekly DT by 0.73 (p value = 0.013). The mobile app-based community is an effective and less resource-intensive tool to increase PA and decrease distress in breast cancer survivors.Trial Registration: NCT03190720, NCT03072966

BackgroundStroke is the leading cause of severe disability and many survivors report long-term physical or cognitive impairments that may impact their ability to achieve community mobility (CM). Purpose: To determine the extent to which people with chronic stroke achieve CM compared to age-matched norms or non-neurologically impaired controls.MethodsThe StrokEDGE outcome measures were searched to identify validated tools that included >25% of items addressing CM. MEDLINE, CINAHL, Google Scholar, PubMed, PEDro and the Cochrane databases were searched from 2001 to 2015 with the identified outcome measures cross-referenced against search terms related to stroke and CM. Inclusion criteria: utilized a validated CM outcome measure, chronic (>3 months post) stroke survivors, and randomized controlled trial, observational or cohort study design. One reviewer screened the studies and performed data extraction and three performed quality appraisal. Fourteen studies met all inclusion criteria.ResultsStroke survivors have impaired CM as demonstrated by 30–83% of normative or non-stroke subject CM scores. As time post-stroke increased, CM improved only slightly. Factors found to correlate with the CM were age, education, general well-being, emotional state, motor function and coordination, independence in activities of daily living, balance, endurance and driving status. Limitations of this review include a relatively high functioning cohort, no meta-analysis and reliance on outcome measures not specifically designed to measure CM.ConclusionSurvivors of stroke may experience a significant decrease in CM compared to people without neurological injury. Rehabilitation addressing motor function, coordination, independence in activities of daily living, balance and endurance may be important for achieving higher levels of CM. Outcome measures directly addressing CM are needed.

Objective: To provide evidence for the validity and reliability of a new outcome measure of balance, the Community Balance and Mobility Scale, developed for the ambulatory individual with traumatic brain injury. Design: A validity and reliability study. Setting: Acute care, in- and outpatient rehabilitation and day hospital settings. Subjects: Two convenience samples (n=36, 32) of ambulatory patients with traumatic brain injury. Main measures: The content and construct validity, test-retest, inter- and intra-rater reliability and internal consistency of the Community Balance and Mobility Scale. Results: Content validity was demonstrated by the involvement of patients with traumatic brain injury (n=7) and clinicians (n=17) in the process of item generation and by physical therapists’ ratings of item relevance. Further support is the correlation of the Community Balance and Mobility Scale scores with physical therapists’ global balance ratings of the patient (r=0.62). Construct validity was supported by the ability of the measure to differentiate between patients along the continuum of care and also by comparisons with maximal walking velocity (r=0.64). Patients who scored greater than or less than 50 on the balance measure demonstrated significantly different Community Integration Questionnaire scores (P=0.004). The Community Balance and Mobility Scale demonstrated intraclass correlation coefficients (ICCs) of 0.977, 0.977, 0.975 and Cronbach’s alpha of 0.96 for intra-, inter-, test-retest reliability and internal consistency, respectively. Conclusion: The Community Balance and Mobility Scale is a valid and reliable outcome measure for the ambulatory individual with traumatic brain injury.

The mix of physical therapy services is thought to be different with different impairment groups. However, it is not clear how much variation there is across impairment groups. Furthermore, the extent to which the same physical therapy activities are associated with functional outcomes across different types of patients is unknown. The purposes of this study were: (1) to examine similarities and differences in the mix of physical therapy activities used in rehabilitation among patients from different impairment groups and (2) to examine whether the same physical therapy activities are associated with functional improvement across impairment groups. This was a prospective observational cohort study. The study was conducted in inpatient rehabilitation facilities. The participants were 433 patients with stroke, 429 patients with total knee arthroplasty (TKA), and 207 patients with traumatic brain injury (TBI). Measures used in this study included: (1) the Comprehensive Severity Index to measure the severity of each patient's medical condition, (2) the Functional Independence Measure (FIM) to measure function, and (3) point-of-care instruments to measure time spent in specific physical therapy activities. All 3 groups had similar admission motor FIM scores but varying cognitive FIM scores. Patients with TKA spent more time on exercise than the other 2 groups (average=31.7 versus 6.2 minutes per day). Patients with TKA received the most physical therapy (average=65.3 minutes per day), whereas the TBI group received the least physical therapy (average=38.3 minutes per day). Multivariate analysis showed that only 2 physical therapy activities (gait training and community mobility) were both positively associated with discharge motor FIM outcomes across all 3 groups. Three physical therapy activities (assessment time, bed mobility, and transfers) were negatively associated with discharge motor FIM outcome. The study focused primarily on physical therapy without concurrently considering other therapies such as occupational therapy, speech-language pathology, nursing care, and case management or the potential interaction of these inputs. This analysis did not consider the interventions that physical therapists used when patients participated in discrete physical therapy activities. All 3 patient groups spent a considerable portion of their physical therapy time in gait training relative to other activities. Both gait training and community mobility are higher-level activities that were positively associated with outcomes, although all 3 groups spent little time in community mobility activities. Further research studies, such as randomized clinical trials and predictive validity studies, are needed to investigate whether higher-level or more-integrated therapy activities are associated with better patient outcomes.

Objective measures of community mobility are advantageous for capturing movement outside the home. Compared with subjective, self-reported techniques, global positioning system (GPS) technologies leverage passive, real-time location data to reduce recall bias and increase measurement precision. We developed methods to quantify community mobility among community-dwelling older adults and assessed how GPS-derived indicators relate to clinical measures of physical and cognitive performance. Participants (n=149; M ± standard deviation [SD]=77.1±6.5years) from the program to improve mobility in aging (PRIMA) study, a physical therapy intervention to improve walking ability, carried a GPS device for 7days. Community mobility was characterized by assessing activity space, shape, duration, and distance. Associations between GPS-derived indicators and cognition and physical function were evaluated using Spearman correlations. In adjusted models, a larger activity space, greater duration (eg, time out-of-home), and greater distance traveled from home were correlated with better 6-Minute Walk Test performance (ρ=0.17-0.23, p's < .05). Amore circular activity shape was related to poorer performance on the Trail Making Test, Part A(ρ=0.18, p < .05). More time out-of-home and a larger activity space were correlated with faster times on the Trail Making Test, Part B (ρ=-0.18 to -0.24, p's < .05). Community mobility measures were not associated with global cognition, skilled walking, or usual gait speed. GPS-derived community mobility indicators capture real-world activity among older adults and were correlated with clinical measures of executive function and walking endurance. These findings will guide the design of future interventions to promote community mobility.

Community mobility is an individual’s movement outside the home. It is essential for the completion of many instrumental activities of daily living, such as shopping and healthcare, and promotes physical function, social engagement, independent living, and quality of life. Mobility research often focuses on gait speed measured in clinical settings, a critical but not sufficient determinant of community mobility. Here we present four talks that assess community mobility and its determinants using novel methodologies to enhance our understanding of how to maintain independence in older ages. First, Andrea Rosso presents characteristics of individuals with the strongest associations between environmental walkability, as assessed by virtual audits, and walking. Second, Kyle Moored demonstrates associations of self-reported fatigability with life space among older men, independent of their physical functioning. Breanna Crane introduces GPS-based objective measures of community mobility and their associations with cognitive and physical function of older adults. Finally, Pam Dunlap presents results of a randomized clinical trial of a physical therapy intervention to improve walking in older adults on subjective and objective measures of life space. These talks will provide a better understanding of the factors related to community mobility, introduce attendees to novel methodologies in the assessment of both community mobility and risk factors associated with the loss of community mobility, and demonstrate approaches to improve community mobility in at-risk older adults. The discussant, Jana Hirsch, will provide perspectives on how these data inform our current view of community mobility and will lead a discussion with the audience.

Community vulnerability and mobility: What matters most in spatio-temporal modeling of the COVID-19 pandemic?

Background: Coronary Artery Bypass Graft Surgery (CABG) is the most commonly performed operation in cardiac surgery and results from isolated CABG are used as benchmark to rate cardiac surgery programs in the US by the Society of Thoracic Surgeons (STS). Accurate and reliable mortality risk prediction of CABG patients is essential for developing targeted treatment strategies. Traditional risk scores such as the STS score and EuroSCORE II offer moderate discriminative value, and have limited utility in predicting outcomes for high-risk patients. Machine learning (ML) models have emerged as an alternate method that may offer improved risk assessment. This study aims to compare machine learning models with traditional risk scores for predicting all-cause mortality in patients undergoing CABG. Methods: PubMed, EMBASE, Web of Science and Cochrane databases were searched until 18th May 2024 for studies comparing ML models with traditional statistical methods for event prediction of CABG patients. The primary outcome was comparative discrimination measured by C-statistics with 95% confidence intervals between ML models and traditional methods in estimating the risk of all-cause mortality. A secondary aim was to compare model calibration between ML models and traditional risk scores, adhering to guidelines for predictive algorithm comparisons. Results: A total of 27 studies were included (568,190 patients). The summary C-statistic of all ML models across all endpoints was 0.82 (95% CI, 0.78-0.85), compared to traditional methods 0.73 (95% CI, 0.70-0.75). The difference in C-statistic between all ML models and traditional methods was 0.09 (p&lt;0.0001). One model undertook external validation, and calibration was inconsistently reported. Conclusion: ML models demonstrated superior discrimination of all-cause mortality for CABG patients compared to traditional risk scores. Whilst there is great potential for ML models to be integrated into electronic healthcare systems to improve pre-operative risk stratification, and guide clinical decision making, the methodological and validation limitations pose a hurdle for immediate clinical implementation.

BackgroundCellular mobile telephone technology shows much promise for delivering and evaluating healthcare interventions in cost-effective manners with minimal barriers to access. There is little data demonstrating that these devices can accurately measure clinically important aspects of individual functional status in naturalistic environments outside of the laboratory.ObjectiveThe objective of this study was to demonstrate that data derived from ubiquitous mobile phone technology, using algorithms developed and previously validated by our lab in a controlled setting, can be employed to continuously and noninvasively measure aspects of participant (subject) health status including step counts, gait speed, and activity level, in a naturalistic community setting. A second objective was to compare our mobile phone-based data against current standard survey-based gait instruments and clinical physical performance measures in order to determine whether they measured similar or independent constructs.MethodsA total of 43 ambulatory, independently dwelling older adults were recruited from Nebraska Medicine, including 25 (58%, 25/43) healthy control individuals from our Engage Wellness Center and 18 (42%, 18/43) functionally impaired, cognitively intact individuals (who met at least 3 of 5 criteria for frailty) from our ambulatory Geriatrics Clinic. The following previously-validated surveys were obtained on study day 1: (1) Late Life Function and Disability Instrument (LLFDI); (2) Survey of Activities and Fear of Falling in the Elderly (SAFFE); (3) Patient Reported Outcomes Measurement Information System (PROMIS), short form version 1.0 Physical Function 10a (PROMIS-PF); and (4) PROMIS Global Health, short form version 1.1 (PROMIS-GH). In addition, clinical physical performance measurements of frailty (10 foot Get up and Go, 4 Meter walk, and Figure-of-8 Walk [F8W]) were also obtained. These metrics were compared to our mobile phone-based metrics collected from the participants in the community over a 24-hour period occurring within 1 week of the initial assessment.ResultsWe identified statistically significant differences between functionally intact and frail participants in mobile phone-derived measures of percent activity (P=.002, t test), active versus inactive status (P=.02, t test), average step counts (P<.001, repeated measures analysis of variance [ANOVA]) and gait speed (P<.001, t test). In functionally intact individuals, the above mobile phone metrics assessed aspects of functional status independent (Bland-Altman and correlation analysis) of both survey- and/or performance battery-based functional measures. In contrast, in frail individuals, the above mobile phone metrics correlated with submeasures of both SAFFE and PROMIS-GH.ConclusionsContinuous mobile phone-based measures of participant community activity and mobility strongly differentiate between persons with intact functional status and persons with a frailty phenotype. These measures assess dimensions of functional status independent of those measured using current validated questionnaires and physical performance assessments to identify functional compromise. Mobile phone-based gait measures may provide a more readily accessible and less-time consuming measure of gait, while further providing clinicians with longitudinal gait measures that are currently difficult to obtain.

The authors compared the effects of a standard strength and endurance intervention with a standard plus timing and coordination training intervention on community mobility measured using global positioning systems (GPS) among community-dwelling older adults in this secondary analysis of a randomized controlled trial. Participants were randomized to a standard or a standard plus timing and coordination training program. Community mobility was measured using the Life Space Assessment (LSA) and GPS indicators of community mobility at baseline, as well as at 12 (immediately after the intervention), 24, and 36weeks. Linear mixed models were used for analysis. There were 166 participants with GPS data at baseline, including 81 in the standard plus group and 85 in the standard group. The groups did not differ in participant characteristics or GPS measures at baseline. There were no significant within-group changes in GPS indicators of community mobility or LSA score over time, nor between-group differences of the same. There were no significant changes in community mobility with either intervention or between-intervention differences. These findings suggest that interventions targeting physical function alone may not be sufficient to improve community mobility or participation in older adults. Future research should focus on the development of multifaceted interventions targeted to improve real-world participation. The studied interventions did not significantly change community mobility measured using GPS-derived community mobility measures or self-report measures in older adults, suggesting that more comprehensive interventions may be needed to target improvements in community mobility.

Gait automaticity may decline with age-related impairments in the brain. As subcortical structures supporting gait automaticity decline, the prefrontal cortex (PFC) may provide compensatory function, allowing for maintenance of mobility but with loss of efficiency. Loss of efficiency may reduce ability to navigate complex community environments and restrict community mobility. We assessed the relation between PFC activation during walking in the laboratory with self-reported and objectively measured community mobility in participants aged 65+ (n=42, mean age=76, 60% female) from a randomized trial of a physical activity intervention to improve walking speed. PFC activation was measured by functional near-infrared spectroscopy as change from quiet standing to usual pace walking. Community mobility was measured objectively by 7-day global positioning system recordings of spatial (standard deviation ellipse area (SDEa), maximum distance from home) and temporal (percent time out of home (pTOH)) characteristics. Additionally, step count was recorded from 7-day actigraphy and the Life-Space Assessment (LSA) assessed self-reported mobility. Among participants with complete data at baseline (n=27), higher PFC activation during walking was associated with smaller SDEa (beta=-0.93 (-1.72, -0.15)), less pTOH (beta=-0.45 (-0.78, -0.12)), and lower step count (beta=-346 (-575, -118)) which persisted after adjusting for age, gender, education, or gait speed. PFC activation was not associated with maximum distance or LSA. There were no significant associations at the post-intervention visits (n=39). Greater PFC activation, likely indicating reduced gait automaticity, is related to lower spatial extent, duration, and intensity of community mobility. This association may be mitigated by participation in physical activity interventions.

Neighborhood walkability may encourage greater out-of-home travel (ie, community mobility) to support independent functioning in later life. We examined associations between a novel walkability audit index and Global Positioning System (GPS)-derived community mobility in community-dwelling older adults. We compared associations with the validated Environmental Protection Agency (EPA) National Walkability Index and further examined moderation by clinical walking speed. Participants were 146 older adults (Mean = 77.0 ± 6.5 years, 68% women) at baseline of a randomized trial to improve walking speed. A walkability index (range: 0-5; eg, land-use mix, crosswalks, and so on) was created using Google Street View audits within 1/8-mile of the home. Participants carried a GPS device for 5-7 days to derive objective measures of community mobility (eg, time spent out of home, accumulated distance from home). Each 1 SD (~1.3-point) greater walkability audit score was associated with a median 2.16% more time spent out of home (95% confidence interval [95% CI]: 0.30-4.03, p = .023), adjusting for individual demographics/health and neighborhood socioeconomic status. For slower walkers (4-m walking speed <1 m/s), each 1 SD greater audit score was also associated with a median 4.54 km greater accumulated distance from home (95% CI: 0.01-9.07, p (interaction) = .034). No significant associations were found for the EPA walkability index. Walkability immediately outside the home was related to greater community mobility, especially for older adults with slower walking speeds. Results emphasize the need to consider the joint influence of local environment and individual functioning when addressing community mobility in older populations.

Objective measures of community mobility are advantageous for capturing life-space activity. In contrast to subjective, self-reported approaches, GPS-derived objective measures leverage passive, real-time data collection techniques to mitigate recall bias and minimize participant burden. We present methods to quantify community mobility among a sample of 164 community-dwelling older adults (Mean age=77.3±6.5) from a physical therapy intervention aimed at improving walking ability. We characterized community mobility using activity space metrics (e.g., standard deviation ellipse (SDE) area), timing (e.g., time outside home), and shape (e.g., SDE compactness). We will discuss challenges and solutions to generating these metrics as well as their associations with physical and cognitive performance. Time outside of home and SDE area, but not SDE compactness, were correlated with better performance on the 6-Minute Walking Test and Trail-Making Test (Part B) (ρ=.20-.23, p’s<.05). These findings will aid in understanding which community mobility measures are associated with functional capacity.

Background/Objectives: Non-variceal upper gastrointestinal bleeding (NVUGIB) is associated with considerable morbidity and mortality, particularly in emergency department (ED) settings. While traditional clinical scores such as the Glasgow-Blatchford Score (GBS), AIMS65, and Pre-Endoscopic Rockall are widely used for risk stratification, their accuracy in mortality prediction is limited. This study aimed to evaluate the performance of multiple supervised machine learning (ML) models in predicting 30-day all-cause mortality in NVUGIB and to compare these models with established risk scores. Methods: A retrospective cohort study was conducted on 1233 adult patients with NVUGIB who presented to the ED of a tertiary center between January 2022 and January 2025. Clinical and laboratory data were extracted from electronic records. Seven supervised ML algorithms—logistic regression, ridge regression, support vector machine, random forest, extreme gradient boosting (XGBoost), naïve Bayes, and artificial neural networks—were trained using six feature selection techniques generating 42 distinct models. Performance was assessed using AUROC, F1-score, sensitivity, specificity, and calibration metrics. Traditional scores (GBS, AIMS65, Rockall) were evaluated in parallel. Results: Among the cohort, 96 patients (7.8%) died within 30 days. The best-performing ML model (XGBoost with univariate feature selection) achieved an AUROC > 0.80 and F1-score of 0.909, significantly outperforming all traditional scores (highest AUROC: Rockall, 0.743; p < 0.001). ML models demonstrated higher sensitivity and specificity, with improved calibration. Key predictors consistently included age, comorbidities, hemodynamic parameters, and laboratory markers. The best-performing ML models demonstrated very high apparent AUROC values (up to 0.999 in internal analysis), substantially exceeding conventional scores. These results should be interpreted as apparent performance estimates, likely optimistic in the absence of external validation. Conclusions: While machine-learning models showed markedly higher apparent discrimination than conventional scores, these findings are based on a single-center retrospective dataset and require external multicenter validation before clinical implementation.

Background: Atrial fibrillation (AF) recurrence after catheter ablation remains difficult to predict. While traditional risk scores such as CHA2DS2-VASc and HATCH are widely used, their predictive accuracy is modest. Machine learning (ML) models have emerged as a potential alternative, integrating multimodal data to enhance individualized risk stratification. We conducted a systematic review and meta-analysis to evaluate their predictive performance, model design, and comparison with clinical risk scores. Methods: We searched PubMed, Embase, and Scopus for studies published between 2013 and 2024 using ML models to predict post-ablation AF recurrence. Eligible studies included adults undergoing catheter ablation and reported validation of ML model performance. Two reviewers independently extracted data on study design, sample size, input features, ML model type, validation method, AUROC, recurrence rates, and comparator clinical scores. Risk of bias was assessed using PROBAST. Results: Eleven studies comprising 2,994 patients were included. Most were retrospective and conducted between 2013 and 2023 across China, the United States, Portugal, and Europe. Sample sizes ranged from 90 to 1,606, with follow-up durations from 6 months to 5.8 years. AF recurrence rates ranged from 21% to 54%. ML model types included gradient boosting (n=4), convolutional neural networks (n=3), logistic regression (n=2), regularized linear models (n=1), and simulation-based models (n=1). Input data varied from clinical variables (age, LA diameter, comorbidities) to ECG morphology, cardiac CT-based LA wall thickness, and electrogram-derived features. In three head-to-head comparisons, ML models outperformed traditional scores. For example, the HAD-AF model achieved an AUROC of 0.938 versus 0.679 for CHA2DS2-VASc. Average patient age ranged from 56 to 66 years, with &gt;60% male across cohorts. The pooled sensitivity and specificity of ML models for predicting AF recurrence were 80.2% (95% CI: 77.7%–82.7%) and 76.5% (95% CI: 73.9%–79.2%), respectively. The pooled AUROC from five studies was 0.89 (95% CI: 0.86–0.92), reflecting strong discriminative ability across diverse populations and input modalities. Conclusions: Machine learning models consistently outperformed traditional scores for predicting AF recurrence after ablation, with pooled AUROC nearing 0.90 and balanced sensitivity/specificity. Standardized external validation is essential for clinical implementation.