On-Yee Lo

Background: Recovery after total hip arthroplasty (THA) and total knee arthroplasty (TKA) is typically assessed with infrequent clinic-based evaluations that may not reflect patients’ everyday mobility. Smartphone inertial sensors enable scalable, repeated gait measurement in real-world settings, but the extent to which prompted walking tests reflect daily life walking during recovery is not well defined. Objective: The objective of this study was to characterize week-by-week recovery trajectories after THA and TKA using high-frequency smartphone sensor data and quantify the difference between prompted in-app walking tests and passive daily life background walking. Methods: We analyzed longitudinal smartphone-based gait measurements from 735 patients (THA, n=350; TKA, n=385) who underwent unilateral surgery between July 2022 and October 2025. Nine spatiotemporal gait parameters were monitored over the first 15 postoperative weeks across 2 conditions: prompted “in-app” walks and passively captured “background” walks. Recovery trajectories were evaluated using Kaplan-Meier time-to-baseline analyses, linear mixed-effects models, and comparison of late-stage plateau levels (weeks 11‐15) using estimated marginal means of the models. Results: Across all parameters, THA patients returned to preoperative baseline levels faster than TKA patients, with mean trajectories generally approaching baseline by week 4 for THA versus weeks 6 to 8 for TKA. Linear mixed-effects models showed that surgical procedure type significantly influenced recovery trajectory for all parameters except single support of the unaffected leg. All gait parameters except stance asymmetry and single support of the unaffected leg exhibited persistent differences through the late-recovery plateau (weeks 11‐15). We also compared prompted “in-app” walks with passively captured background walking. Prompted and passive walking showed systematic divergence in both THA and TKA; prompted assessments generally indicated faster or more complete return to baseline than passive daily life walking. Finally, preoperative gait level was strongly associated with postoperative trajectories, with baseline-stratified analyses showing distinct patterns of recovery between groups. Conclusions: High-frequency smartphone gait assessment quantifies procedure-specific recovery dynamics and demonstrates that prompted tests and passive daily life measurements capture distinct, noninterchangeable functional signals. Parameters from prompted walks plateaued while those from passively captured walks were still changing; prompted testing alone therefore captures only one component of returning function, highlighting the additional information that passive, real-world gait monitoring can provide.

Background: Standing is a complex task often performed concurrently with a cognitive task. This type of “dual-tasking” requires attentional processing and related patterns of brain activation, particularly in the gamma frequency band of electrographic electroencephalography (EEG) signals (30-50 Hz). In older adults, transcranial direct current stimulation (tDCS) designed to facilitate the excitability of the left dorsolateral prefrontal cortex (L-DLPFC) improves attentional control and dual-task standing performance when tested just after stimulation. However, the effects of tDCS on underlying brain activity during single- and dual-task standing are unknown. Methods: Thirty older adults completed three visits during which they received 20 min of tDCS targeting the L-DLPFC, the primary sensorimotor cortex (SM1, active control), or sham (inactive control) in randomized order. Before and immediately after each stimulation session, 32-channel EEG and body-worn motion sensors were used as participants completed trials of standing with and without verbalized serial subtractions. Results: tDCS targeting L-DLPFC, as compared to SM1 and sham stimulation, increased gamma power during single-task standing in the anterior left (p = .02) and anterior right regions (p = .03). A similar trend (p = .08) towards increased gamma power in these regions following tDCS targeting the L-DLPFC was also observed during dual-task standing. Participants who demonstrated a greater increase in gamma power after tDCS targeting L-DLPFC exhibited greater reduction (improvement) in postural sway during single-task standing (r = −0.53, p = .007) and dual-task standing (r = −0.51, p = .003). Conclusions: These results suggest that tDCS designed to facilitate L-DLPFC excitability modulates gamma power during single-task standing, which was in turn correlated with improved postural control, in older adults.

BACKGROUND Recovery after total hip (THA) and total knee arthroplasty (TKA) is typically assessed with infrequent clinic-based evaluations that may not reflect patients’ everyday mobility. Smartphone inertial sensors enable scalable, repeated gait measurement in real-world settings, but the extent to which prompted walking tests reflect daily life walking during recovery is not well defined. OBJECTIVE The objective of this study was to characterize week-by-week recovery trajectories after THA and TKA using high-frequency smartphone sensor data and quantify the signal divergence between prompted in-app walking tests and passive daily life background walking. METHODS Methods We analyzed longitudinal smartphone-based gait measurements from 743 patients (THA, n=355; TKA, n=388) who underwent unilateral surgery between July 2022 and October 2025. Nine spatiotemporal gait parameters were monitored over the first 15 postoperative weeks across two conditions: prompted "in-app" walks and passively captured "background" walks. Recovery trajectories were evaluated using Kaplan-Meier time-to-baseline analyses, linear mixed-effects models, and two-sample t-tests comparing late-stage plateau levels (weeks 11-15). RESULTS Across all parameters, THA patients returned toward preoperative levels faster than TKA patients, with mean trajectories approaching baseline by week 4 for THA versus weeks 6-8 for TKA. Linear mixed-effects models showed that surgical procedure type significantly influenced recovery for all parameters except single support of the unaffected leg. Several metrics, including walk speed, cadence, and stride length, exhibited persistent differences through the late-recovery plateau (weeks 11-15). We also compared prompted "in-app" walks with passively captured background walking. Prompted and passive walking showed systematic divergence in both THA and TKA; prompted assessments generally indicated faster or more complete recovery than passive daily life walking. Finally, preoperative gait level in all conditions was strongly associated with postoperative trajectories, with baseline-stratified analyses showing distinct patterns of recovery between groups. CONCLUSIONS High-frequency smartphone gait assessment quantifies procedure-specific recovery dynamics and demonstrates that prompted tests and passive daily life measurements capture non-interchangeable functional signals. These findings suggest that declaring "full recovery" based solely on prompted functional capacity may overlook lingering habitual performance compensations.

Objective: We present the rationale, design, and methods for the Healthy Aging Initiative (HAI), a prospective longitudinal cohort study of older adults primarily residing in senior housing, aimed to identify and characterize factors associated with a prolonged healthspan in this population. Methods: The HAI, developed with input from experts at the Marcus Institute for Aging Research and informed by a pilot study, includes a community engaged recruitment method and a yearly assessment encompassing the following domains: sociodemographic information, medical history, lifestyle, psychological well-being, physical and cognitive health, mobility, and sensory health. Recruitment is ongoing and includes participants who are aged ≥55 years recruited from six senior housing communities. A control group of community-dwelling participants living in conventional housing aged ≥55 is also planned. Results: Recruitment remains ongoing. Expected results include characterizing the sociodemographic and health profiles of older adults in independent-living (IL) senior housing, identifying psychological, lifestyle and biological factors associated with a prolonged healthspan, and defining high-risk subgroups to inform future targeted interventions and health promotion strategies. Data will be made publicly available. Discussion: Longitudinal studies on aging often face challenges such as retention and sustained community engagement. Through a collaborative effort among research scientists in the aging field, housing providers, and older adults, this project aims to collect data on aging trajectories among older adults residing in IL senior housing to inform future initiatives that promote prolonged healthspan in this population.

Background: Insufficient physical activity in older adults remains a global health issue. Several interrelated factors contributing to inactivity are linked to the prefrontal cortex. We conducted a pilot study to assess the feasibility, acceptability, and effects of combining transcranial direct current stimulation (tDCS) and behavior counseling to improve physical activity in older adults. Methods: Inactive older adults living in subsidized housing participated in this randomized controlled trial. Baseline physical activity (daily steps) was measured with a Fitbit for 2 weeks. Participants then received an 8-week intervention, including 10 daily sessions of tDCS or Sham stimulation during the first 2 weeks, along with 4 biweekly behavior sessions. Functional outcomes were assessed at baseline, poststimulation, and after the entire intervention. Step counts were measured throughout the intervention and a 12-week retention period. Results: Twenty-eight participants completed the study. Compliance was 97%, 93%, and 92% for brain stimulation, behavior sessions, and follow-up assessments, respectively. Fitbit adherence was 96% and 71% during the intervention and retention periods. The tDCS arm, compared to Sham, exhibited greater increase in average daily steps (p .001). Participants increased 1 179 (+ 22%) and 550 (+ 15%) steps/day from baseline in the tDCS and Sham arms, respectively. Motivation (p .03) and self-reported walking performance (p .02) were also improved in the tDCS arm compared to Sham. Conclusions: Combining tDCS and personalized behavior counseling to improve physical activity was feasible, acceptable, and appeared to be effective in a cohort of inactive older adults living within subsidized housing. Larger and more definitive studies are warranted.

Background: Gait speed is a valuable biomarker for mobility and overall health assessment. Existing methods to measure gait speed require expensive equipment or personnel assistance, limiting their use in unsupervised, daily-life conditions. The availability of smartphones equipped with a single inertial measurement unit (IMU) presents a viable and convenient method for measuring gait speed outside of laboratory and clinical settings. Previous works have used the inverted pendulum model to estimate gait speed using a non–smartphone-based IMU attached to the trunk. However, it is unclear whether and how this approach can estimate gait speed using the IMU embedded in a smartphone while being carried in a pants pocket during walking, especially under various walking conditions. Objective: This study aimed to validate and test the reliability of a smartphone IMU–based gait speed measurement placed in the user’s front pants pocket in both healthy young and older adults while walking quietly (ie, normal walking) and walking while conducting a cognitive task (ie, dual-task walking). Methods: A custom-developed smartphone application (app) was used to record gait data from 12 young adults and 12 older adults during normal and dual-task walking. The validity and reliability of gait speed and step length estimations from the smartphone were compared with the gold standard GAITRite mat. A coefficient-based adjustment based upon a coefficient relative to the original estimation of step length was applied to improve the accuracy of gait speed estimation. The magnitude of error (ie, bias and limits of agreement) between the gait data from the smartphone and the GAITRite mat was calculated for each stride. The Passing-Bablok orthogonal regression model was used to provide agreement (ie, slopes and intercepts) between the smartphone and the GAITRite mat. Results: The gait speed measured by the smartphone was valid when compared to the GAITRite mat. The original limits of agreement were 0.50 m/s (an ideal value of 0 m/s), and the orthogonal regression analysis indicated a slope of 1.68 (an ideal value of 1) and an intercept of −0.70 (an ideal value of 0). After adjustment, the accuracy of the smartphone-derived gait speed estimation improved, with limits of agreement reduced to 0.34 m/s. The adjusted slope improved to 1.00, with an intercept of 0.03. The test-retest reliability of smartphone-derived gait speed was good to excellent within supervised laboratory settings and unsupervised home conditions. The adjustment coefficients were applicable to a wide range of step lengths and gait speeds. Conclusions: The inverted pendulum approach is a valid and reliable method for estimating gait speed from a smartphone IMU placed in the pockets of younger and older adults. Adjusting step length by a coefficient derived from the original estimation of step length successfully removed bias and improved the accuracy of gait speed estimation. This novel method has potential applications in various settings and populations, though fine-tuning may be necessary for specific data sets.

The four-meter gait speed (4MGS) is a recommended physical performance test in older adults but is challenging to implement clinically. We developed a smartphone application (App) with a four-meter ribbon for remote 4MGS testing at home. This study aimed to assess the validity and reliability of this smartphone App-based assessment of the home 4MGS. We assessed the validity of the smartphone App by comparing it against a gold standard video assessment of the 4MGS conducted by study staff visiting community-dwelling older adults and against the stopwatch-based measurement. Moreover, we assessed the test–retest reliability in two supervised sessions and three additional sessions performed by the participants independently, without staff supervision. The 4MGS measured by the smartphone App was highly correlated with video-based 4MGS (r = 0.94), with minimal differences (mean = 0.07 m/s, ± 1.96 SD = 0.12) across a range of gait speeds. The test–retest reliability for the smartphone App 4MGS was high (ICC values: 0.75 to 0.93). The home 4MGS in older adults can be measured accurately and reliably using a smartphone in the pants pocket and a four-meter strip of ribbon. Leveraging existing technology carried by a significant portion of the older adult population could overcome barriers in busy clinical settings for this well-established objective mobility test.

BACKGROUND Gait speed is a valuable biomarker for mobility and health assessment in clinical and research settings. Existing methods to measure gait speed require expensive equipment or personal assistance, limiting their use in unsupervised, daily-life conditions. OBJECTIVE This study aimed to examine the validity and reliability of the inverted pendulum model approach to estimating gait speed and required spatial information (i.e., step length) from data collected with a single smartphone IMU placed in the user’s pants pocket, for both healthy younger and older adults. METHODS A custom-developed smartphone app was used to record gait data from healthy younger and older adults during normal and dual-task walking. Validity and reliability of gait speed and step length estimations from the smartphone were compared with the gold-standard GAITRite mat. A coefficient-based adjustment based upon a coefficient relative to the original estimation of step length was applied to improve the accuracy of gait speed estimation. RESULTS Smartphone-derived gait speed and step length showed good validity compared to the GAITRite mat, with minimal bias and acceptable limits of agreement. The test-retest reliability of app-derived gait speed was good to excellent within supervised laboratory settings and unsupervised home conditions. After adjustment, the accuracy of gait speed estimation improved, with reduced bias. The adjustment coefficients were applicable to a wide range of step lengths and gait speeds. CONCLUSIONS The inverted pendulum approach is a valid and reliable method for estimating gait speed from a smartphone IMU placed in the pocket of younger and older adults. Adjusting step length by a coefficient derived from the original estimation of step length successfully removed bias and improved the accuracy of gait speed estimation. This novel method has potential applications in various settings and populations, though fine-tuning may be necessary for specific datasets.