Is Maximal or Usual Walking Speed from Large Scale Wrist Sensor Data Better at Predicting Dementia, Depression and Death?

Older people are at increased risk of many adverse health outcomes, including dementia and depression, that burden the global health system. This paper presents algorithms for the large-scale assessment of daily walking speeds. We hypothesize that (i) data from wrist-worn sensors can be used to assess walking speed accurately; and that (ii) maximal daily walking speed is a better predictor of health outcomes than usual daily walking speed. First, algorithms were developed and tested using data from 101 participants aged 19 to 91 (47 ± 18) years. Participants wore an AX3 accelerometer (Axivity, UK) on their dominant wrist while undertaking daily life activities with electronic walkway data used for ground truth. Subsequently, prediction models for dementia, depression and death were developed using the data of 47,406 participants (≥ 60 years) from the UK Biobank study. Daily walking speeds were derived from 7-day AX3 data with time-to-events using electronic health records. The accuracy of derived walking speeds was assessed using root mean square error (RMSE). Time-to-events were modelled using Cox regression with inverse hazard ratios reported for univariable models and Harrell's concordance for multivariable models. Derived walking speeds had an RMSE of between 3% and 4% depending on arm position. We found that for simple models, maximal walking speed was significantly better than usual walking speed at predicting time to dementia (1.62 vs 1.34), depression (1.29 vs 1.17) and death (1.56 vs 1.27). However, the addition of known risk factors in subsequent multivariable models reduced the apparent benefit of using maximal as opposed to usual daily walking speed as the gait parameter. In summary, walking speed was accurately measured with a wrist-worn device, and maximal daily waking speed may be better than usual daily walking speed at predicting some adverse health outcomes.Clinical Relevance - This study demonstrated the validity of using a simple and unobtrusive wrist-worn sensor to remotely assess daily walking speed. As a single, modifiable and easily understood measure, maximal walking speed was shown to be better than usual walking speed at predicting time-to-dementia, depression and death. Therefore, the inclusion of maximal daily walking speed into screening programs and clinical interventions presents a promising area for further research

Daily-Life Walking Speed, Quality and Quantity Derived from a Wrist Motion Sensor: Large-Scale Normative Data for Middle-Aged and Older Adults

Walking is crucial for independence and quality of life. This study leverages wrist-worn sensor data from UK Biobank participants to establish normative daily-life walking data, stratified by age and sex, to provide benchmarks for research and clinical practice. The Watch Walk digital biomarkers were developed, validated, and applied to 92,022 participants aged 45–79 who wore a wrist sensor for at least three days. Normative data were collected for daily-life walking speed, step-time variability, step count, and 17 other gait and sleep biomarkers. Test–retest reliability was calculated, and associations with sex, age, self-reported walking pace, and mobility problems were examined. Population mean maximal and usual walking speeds were 1.49 and 1.15 m/s, respectively. The daily step count was 7749 steps, and step regularity was 65%. Women walked more regularly but slower than men. Walking speed, step count, longest walk duration, and step regularity decreased with age. Walking speed is associated with sex, age, self-reported pace, and mobility problems. Test–retest reliability was good to excellent (ICC ≥ 0.80). This study provides large-scale normative data and benchmarks for wrist-sensor-derived digital gait and sleep biomarkers from real-world data for future research and clinical applications

Development and large-scale validation of the Watch Walk wrist-worn digital gait biomarkers

Digital gait biomarkers (including walking speed) indicate functional decline and predict hospitalization and mortality. However, waist or lower-limb devices often used are not designed for continuous life-long use. While wrist devices are ubiquitous and many large research repositories include wrist-sensor data, widely accepted and validated digital gait biomarkers derived from wrist-worn accelerometers are not available yet. Here we describe the development of advanced signal processing algorithms that extract digital gait biomarkers from wrist-worn devices and validation using 1-week data from 78,822 UK Biobank participants. Our gait biomarkers demonstrate good test–retest-reliability, strong agreement with electronic walkway measurements of gait speed and self-reported pace and significantly discriminate individuals with poor self-reported health. With the almost universal uptake of smart-watches, our algorithms offer a new approach to remotely monitor life-long population level walking speed, quality, quantity and distribution, evaluate disease progression, predict risk of adverse events and provide digital gait endpoints for clinical trials

A role for STAT3 in IL-10 downregulation of IFN-γ-induced MHC class II molecule expression on primary human blood macrophages

Open Access JournalPaper Presentation: no. 5postprintThe 3rd Annual Scientific Meeting and 4th Annual Meeting of the Hong Kong Society for Paediatric Immunology and Infectious Diseases, Hong Kong, China, 20 March, 2010. In Hong Kong Journal of Psediatrics (New Series), 2010, v. 15 n. 3, p. 25

Short Daily-Life Walking Bouts and Poor Self-Reported Health Predict the Onset of Depression in Community-Dwelling Older People: A 2-Year Longitudinal Cohort Study

Objectives: This study aimed to assess whether the amount and quality of daily-life walking obtained using wearable technology can predict depression onset over a 2-year period, independently of self-reported health status. Design: Longitudinal cohort study. Setting and Participants: Three-hundred twenty-two community-dwelling older people recruited in Sydney, Australia. Methods: Participants were assessed at baseline on (1) depressive symptoms using the Patient Health Questionnaire–9; (2) average weekly physical activity levels over the past month using the Incidental and Planned Activity Questionnaire, (3) clinical mobility tests (ie, short physical performance battery, timed up-and-go test, 6-m walk test); and (4) amount and quality of daily-life walking assessed with a trunk accelerometer (MoveMonitor, McRoberts) for 1 week. Participants were followed up for onset of depressive symptoms for 2 years at 6-monthly intervals. Results: Daily-life walking (ie, gait intensity in the mediolateral axis, daily step counts, duration of longest walk) and self-rated health predicted the new onset of depressive symptoms at 2 years in univariable logistic regression models. In multivariable models containing a self-rated health measure, clinical mobility tests were not predictive of the onset of depressive symptoms. In contrast, a measure of daily-life walking (duration of longest walking bout) was identified as a significant predictor of depressive symptom onset [standardized odds ratio (SOR) 2.44, 95% CI 1.62-3.76] independent of self-rated health (SOR 1.51, 95% CI 1.16-1.96), with these 2 measures achieving a satisfactory prediction accuracy (area under the curve = 0.67, sensitivity: 0.78, specificity: 0.52). Conclusions and Implications: A risk algorithm based on daily-life walking bouts and self-reported health demonstrated good accuracy for the prediction of depression onset in older people over 2 years. Wearable sensor data compared favorably with clinical mobility screens and may add important independent information for screening for depression among older people

Psychometric properties of the caregiver inventory for measuring caregiving self-efficacy of caregivers of patients with palliative care needs

Taking care of patients with palliative care needs could be a stressful event. While caregiving was associated with decreases in psychological health in caregivers, increased caregiving self-efficacy associated with reduced burden. Yet, there is no instrument available in Chinese for assessing caregiving self-efficacy in the palliative care setting. This study aimed to examine the psychometric properties of a Chinese version of Caregiver Inventory (CGI) in Chinese caregivers of patients with palliative care needs. The CGI was translated to the Chinese language, validated by an expert panel, and tested. A convenience sample of 232 patient-caregiver dyads recruited from three hospitals in Hong Kong was included in the analysis. A high completion rate of 95.5% in caregivers and no floor or ceiling effects were noted for the CGI. In contrast to the four-factor structure identified in the original 21- item CGI, our EFA produced an 18-item solution accounting for 57% of the total variation comprising three factors: (1) Care of the care recipient, (2) Managing information and self-care, and (3) Managing emotional interaction with care recipient (C-CGI-18). Separate dimensions for Managing information and Self-care were not supported. For the three domains of the C-CGI-18, Cronbach’s alphas ranged from 0.84 to 0.90 and 2-week testretest reliability ranged from 0.71 to 0.76. Correlations of the three domains with caregiver strain (r: -0.31 to -0.42, p-values<0.01) and total scores in perceived social support (r: 0.24 to 0.36, p-values<0.01). Correlation between the Care of the care recipient domain and patient’s physical functioning (r=0.17, p-value<0.05) indicated acceptable construct validity. In conclusion, the C-CGI-18 has suitable factor structure and psychometric properties for use in assessing caregiving self-efficacy among Chinese caregivers of patients with palliative care needs. It is simply and easy to use and can be recommended for clinical and research practice for the Hong Kong Chinese populations

Factors associated with loss of white matter anisotropy in post-treatment medulloblastoma survivors

We evaluate effects of age at cranial irradiation, time interval since irradiation and irradiation dose on white matter anisotropy in childhood medulloblastoma survivors by computing white matter fractional anisotropy (WM FA) using SPM post-processing functions. Mean percentage change in WM FA of patients compared to controls was -4.4% (sd=7.6%). Using Spearmanís correlation, there were significant associations between percentage reduction of WM FA and age at cranial irradiation (r=0.673, p=0.002) and irradiation dose (r=-0.723, p=<0.001), but not with time interval since irradiation. Multivariate regression analysis confirmed that both factors correlated significantly with percentage reduction of WM FA (adjusted r2=0.516, p=0.001).published_or_final_versio

Adaptation and validation of the Chinese version of Dyspnoea-12 scale in individuals with chronic obstructive pulmonary disease

Introduction: Dyspnoea-12 scale is a validated assessment tool, capturing the perception of dyspnoea and its physical and affective effects in individuals with chronic obstructive pulmonary disease (COPD). A validated version for the Chinese-speaking population has been unavailable. Objective: To develop a Chinese version of D-12 (D-12-C) scale and evaluate its validity and reliability. Methods: D-12 was translated from English to traditional Chinese in collaboration with a physician and a linguist. Back translation was adopted to ensure accuracy of the translation. A total of 155 COPD patients were recruited to test the reliability and validity of the D-12-C scale. Internal reliability and test-retest reliability were measured with Cronbach's alpha coefficient and intra-class correlation coefficient, respectively. Construct validity was assessed through exploratory factor analysis (EFA). Concurrent validity was assessed by the correlation of D-12-C total score and sub-scores and the Chinese version of Saint George's Respiratory Questionnaire (SGRQ), 36-Item Short Form Health Survey (SF-36), COPD Assessment Test (CAT) and Hospital Anxiety and Depression Scale (HADS) total score and sub-scores. Results: The two-factor structure of D-12-C was confirmed by EFA. D-12-C and its sub-scores demonstrated high level of internal reliability (Cronbach's alpha = 0.88) and moderate level of test-retest reliability. D-12-C total score, physical and affective sub-scores were significantly correlated to SGRQ total score (rs = 0.59, p < 0.001) and activity sub-score (rs = 0.38, p = 0.006), SF-36 mental health sub-score (rs = −0.36, p < 0.001), CAT (rs = 0.56, p < 0.001), HADS anxiety (rs = 0.51, p < 0.001) and depression sub-scores (rs = 0.44, p < 0.001). Conclusion: D-12-C scale was developed, which demonstrated satisfactory reliability and validity in measuring dyspnoea among COPD patients

Impact of mobile phone use on accidental falls risk in young adult pedestrians

Background: Mobile phone use is known to be a distraction to pedestrians, increasing their likelihood of crossing into oncoming traffic or colliding with other people. However, the effect of using a mobile phone to text while walking on gait stability and accidental falls in young adults remains inconclusive. This study uses a 70 cm low friction slip hazard and the threat of hazard to investigate the effects of texting while walking on gait stability, the ability to recover balance after a slip hazard and accidental falls. Methods: Fifty healthy young adults performed six walking tasks, and one seated texting task in random order. The walks were conducted over a 10-m walkway. Four progressive hazard levels were used: 1) Seated; 2) Normal Walk (walking across the walkway with no threat of a slip); 3) Threat (walking with the threat of a slip); and 4) Slip (walking with an actual 70 cm slip hazard). The three walking conditions were repeated twice with and without the mobile phone texting dual-task. Gait kinematics and trunk posture were recorded using wearable sensors attached to the head, trunk, pelvis and feet. Study outcomes were analyzed using repeated measures analysis of variance with significance set to P≤.05. Results: Mobile phone use significantly impaired postural balance recovery when slipping, as demonstrated by increased trunk sway. Mobile phone use negatively impacted gait stability as demonstrated by increased step time variability and decreased harmonic ratios. Increased hazard levels also led to reduced texting accuracy. Conclusions: Using a mobile phone to text while walking may compete with locomotor tasks, threat assessment and postural balance control mechanisms, which leads to an increased risk of accidental falls in young adults. Pedestrians should therefore be discouraged through new educational and technology-based initiatives (for example a “texting lock” on detection of walking) from texting while walking on roadside footpaths and other environments where substantial hazards to safety exist

Clinical significance of frizzled homolog 3 protein in colorectal cancer patients

2013-2014 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe