The Modular Actigraphy Platform: A Data Science Solution for Processing High-Resolution Time Series Sensor Data for Sleep and Physical Activity Assessment

1437P - The association between wearable device physical activity metrics and performance status in oncology: A systematic review

Research highlights the potential role of physical activity (PA) in preventing chronic pain and reducing both acute and persistent pain symptoms. While increasing PA is an evidence-based intervention for chronic pain, the relationship between pain processing and PA remains unclear. This study aimed to investigate associations of both PA and fitness assessments with multiple measures of pain sensitivity, hypothesizing links with both static and dynamic pain sensitivity metrics. Sixty-four healthy adults (30 female) representing both high and low activity levels completed a series of pressure-based quantitative sensory tests (QST), including static measures (pressure pain thresholds [PPTs]) and dynamic measures (temporal summation [TS] and conditioned pain modulation [CPM]). PA was evaluated using the International Physical Activity Questionnaire (IPAQ, self-report) and accelerometry (objective). Cardiorespiratory fitness (CRF) was assessed using the YMCA step test. Correlation and regression analyses were used to evaluate these relationships. Higher PPTs were related to more self-reported moderate-to-vigorous PA, vigorous PA, and total PA; accelerometry vigorous PA; and high CRF (p ≤ 0.01). Self-reported vigorous PA was inversely correlated with TS (p = 0.01), while the other PA or CRF metrics were not significantly associated with either TS or CPM (p ≥ 0.04). CRF or vigorous PA metrics were more consistently related to static pressure QST (PPT) than to dynamic QST (TS and CPM). Our findings in a single cohort mirror the inconsistencies noted across cohorts in the literature, suggesting that PA and CRF exhibit distinct relationships with various QST measures.

Physical activity (PA) is a fundamental and modifiable component of healthy aging.1 Hearing impairment (HI) is a common condition at older ages. Several consequences of HI are hypothesized to be mediated by lower PA. However, whether older adults with HI have lower levels of PA is not well understood. We conducted a systematic review of the association between HI and objectively measured PA among adults older than 50. Using controlled vocabulary for HI and PA concepts, we conducted a literature search in Medline (Ovid), Embase, and Cochrane Library (Appendix S1). The results were merged using EndNote (Clarivate Analytics) and imported into Covidence (Veritas Health Innovation). We followed a dual blinded review process for screening; a third reviewer resolved discrepancies. The final decision to include/exclude was based on the prespecified criteria. Of 1127 unique studies, 9 were selected by reviewers and 4 were excluded after a full-text review (different outcome = 3, different exposure = 1), leaving 5 studies. The included studies (Table 1) had between 184 and 1669 participants; all were cross-sectional and used the same data source—the National Health and Nutrition Examination Survey (NHANES) 2003–2006 cycle. One study included only participants with diabetes. Mild hearing impairment not associated with PA category Moderate hearing impairment associated with greater odds of being in the next lower PA category (OR = 1.85 [95% CI:1.01, 3.41]) but no longer in a model that included BMI (OR = 1.70 [95% CI: 0.99, 2.91]) (1) HI categories: normal hearing, mild, and moderate or greater impairment (2) Normal hearing versus any hearing impairment (3) BPTA, continuously Hearing not associated with MVPA minutes: IRR 0.97 [95% CI: 0.88, 1.08] Significant interaction term (hearing impairment × vision impairment) Mild hearing impairment associated with 0.07 [95% CI: 0.01, 0.60] times fewer MVPA minutes Moderate or greater hearing impairment associated with 0.06 [95% CI: 0.01, 0.54] times fewer MVPA minutes Audiometry was performed by trained NHANES examiners.2 Hearing was defined by the speech-frequency (500, 1000, 2000, and 4000 Hz) pure-tone average of the better hearing ear and categorized into normal hearing (≤25 dB HL), mild HI (>25–40 dB HL), and moderate or greater HI (>40 dB HL). PA was assessed, for seven consecutive days, using a hip-worn accelerometer (AM-7164, Actigraph, Pensacola, FL), only removed during sleep and water-based activities. PA was summarized into counts per minute (CPM). Studies calculated the time spent at different activity intensities, using predefined CPM thresholds: sedentary (<100), light (100–2019), moderate (2020–5998), and vigorous (≥5999).3 Two studies used moderate and vigorous (MVPA) minutes per day as an integer variable for their outcome; two studies used MVPA minutes to categorize participants into inactive (0 MVPA min/week), insufficiently active (1–149 MVPA min/week), and sufficiently active (≥150 MVPA min/week).1 Finally, one study used log-transformed sedentary minutes. The studies' findings are summarized in Table 1. One study4 investigated the association between hearing and vision impairments and time spent in MVPA. Neither mild nor moderate or greater HI were associated with PA. Another study5 investigated the association between HI and MVPA minutes among participants with diabetes. Mild and moderate or greater HI were associated with fewer MVPA minutes versus normal hearing. Of the two studies that used this metric, one6 found increased odds of being in the next lower activity group (e.g., inactive vs. insufficiently active) for those with moderate or greater HI versus normal hearing. However, after BMI adjustment, the association was no longer significant. The second study7 also found increased odds of being in the next lower PA group for those with moderate or greater HI versus normal hearing. Both studies found no significant association between mild HI and PA. The last study used log-transformed sedentary minutes (LTSM)8 and found that participants aged >65 years with normal hearing engaged in 0.07 (95% CI: −0.12, −0.01) fewer LTSM per day. Five studies analyzed the association of HI with objectively measured PA, all using NHANES data. The studies suggest that older adults with HI, particularly moderate or greater, may have lower PA levels and spend more time sedentary. However, two studies found no statistically significant associations.4, 6 Moreover, the PA metrics used in these studies have limitations. First, the validity of the CPM thresholds to classify PA intensities for older adults is unknown. Second, MVPA may be unattainable in the study population due to limited functional status. Third, using only MVPA minutes ignores differences in light intensity activity—the most common form of PA among older adults. Finally, emerging research suggests that novel PA metrics such as total PA volume, diurnal patterns, and fragmentation of activity may provide more discriminatory power in older populations than traditional PA measures.9 A study published after our search was completed investigated the association between HI and these novel PA metrics, finding that HI was associated with lower total PA volume and more PA fragmentation.10 Five studies investigated the association between HI and objectively measured PA in NHANES. Overall, hearing impairment was associated with lower levels of PA. The analyses and interpretation of some accelerometry metrics had limitations. Studies that use novel accelerometry-derived PA metrics are warranted. The authors thank Lori Rosman, MLS from the Johns Hopkins Welch Medical Library for her guidance and support in the development of our search strategy. Frank R. Lin is a consultant to Frequency Therapeutics, received speaker honoraria from Caption Call, and director of the Cochlear Center for Hearing and Public Health—a public health research center funded in part by a philanthropic gift from Cochlear Ltd to the Johns Hopkins Bloomberg School of Public Health. All other authors have nothing additional to disclose. Pablo Martinez-Amezcua and Jennifer A. Deal conceived and designed the study. Pablo Martinez-Amezcua, Jonathan J. Suen, and Jennifer A. Deal conducted the title/abstract screening and full-text review of selected papers. Pablo Martinez-Amezcua wrote the manuscript, and all authors reviewed the study design, inclusion/exclusion criteria, and made crucial revision to the manuscript. The authors' sponsors had no role in the design, methods, data collection, analysis, or preparation of the paper. Appendix S1. Systematic literature search strategy for hearing impairment and objectively measured physical activity. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

I. The Problem: Childhood Obesity—A Burgeoning Epidemic e490II. Childhood Obesity Research Summit.........e490III. Pathophysiology and Morbidity ofChildhood Obesity e490A. Pathophysiology of Obesity e490B. Medical Consequences of Childhood Obesity. . .e490C. Psychosocial and Societal Consequences ofChildhood Obesity e490IV. Current Healthcare Practices in ChildhoodObesity Prevention and Treatment e492A. Lifestyle Assessment: Diet and Physical Activity . . . .e492B. Clinical Assessment e493C. Psychosocial/Behavioral Assessment .......e493D. Implementing Effective Prevention andTreatment Options: Diet e494E. Implementing Effective Prevention and TreatmentOptions: Exercise and Sedentary Behaviors . . . .e495F. Implementing Effective Prevention TreatmentOptions: Family and Behavioral Approaches . . .e496G. Behavioral Management e497H. Medical and Pharmacological Management. . . .e497I. Surgical Management e498V. Barriers to Optimum Care e498A. Healthcare System Factors e4981. Preparedness of Providers e4982. Models of Coverage and Care e4993. Uninsurance and Underinsurance AmongChildren and Families e5004. Healthcare Providers and Settings asRole Models e500B. The Challenge of Behavior Change ........e5011. Motivational Interviewing for Pediatric Obesity. .e5012. Psychosocial Factors InfluencingBehavior Change e5023. Environmental Factors InfluencingBehavioral Change e503VI. Practice-Based Resources for Preventionand Management e503A. Models From Other Disciplines e5031. Adult Chronic Care Model e5032. Treatment of Heart Failure e5043. Treatment of Type 2 Diabetes Mellitus .....e5044. Treatment of Childhood Asthma.........e505B. Public Health Measures forChildhood Obesity e5061. Tobacco Control: Implications forChildhood Obesity e506C. Specific Research Issues e5061. Specifying Priorities in the Context of aComplex, Multifactorial Problem ........e5062. Taking a Developmental Approach .......e5073. Engaging Stakeholders e5074. Targeting the Appropriate Outcome .......e508VII. Research Challenges e509A. Research in the Busy Practice e509B. Bring Research Into Practices: Web-BasedAssessment for the PediatricObesity Clinic e509C. Research Agenda: NHLBI and thePediatric Heart Network e510D. Research Agenda: The Children’sOncology Group e510E. Research Agenda: CDC e511F. Research Agenda: Foundations e511VIII. Research and Policy: KnowledgeTranslation e512A. From Practice to Policy to Practice ........e512IX. Concluding Remarks e512Appendix A: Planning Committee e513Appendix B: Presenters e513

Physical activity metrics to predict function in older adults with lower extremity conditions: The Osteoarthritis Initiative

Assessment of physical activity in a free-living environment is important for understanding relations between physical activity and health and determining the effectiveness of interventions. Techniques include behavioral observation, questionnaires in the form of diaries, recall questionnaires and interviews, and physiological markers like heart rate, calorimetry, and motion sensors. The doubly labeled water method has become the gold standard for the validation of field methods of assessing physical activity. Then, questionnaires show a low reliability and validity but can be adequately applied as an activity-ranking instrument. The heart rate requires individual calibration to be an effective method to assess physical activity only at group level. The indicated method for the assessment of habitual physical activity in daily life is a doubly labeled water validated accelerometer. Future developments are simultaneous measurement of body acceleration and heart rate for the assessment of physical fitness. A new generation of accelerometers will provide information on body posture and activity recognition to allow objective assessment of subjects' habitual activities, options for a healthy change, and effects of the follow-up of any changes.

Most of the studies applying the accelerometers to quantify physical activity (PA) and sedentary time (ST) require participants to wear the devices during waking hours only. There is no consensus on how many wearing hours are enough to reflect daily activity behavior in free-living conditions. Whether wear time has substantial impact on accelerometer-based estimates remains unclear. PURPOSE: To examine whether accelerometer-derived metrics were dependent on daily wear time. METHODS: Baseline data from 120 children (24.2% boys) aged 8 to 11 years who participated in the longitudinal study on active travel were analyzed. They were instructed to wear an ActiGraph wGT3x accelerometry on the waist for 7 to 10 consecutive days, only removing it while swimming and bathing. Accelerometer-derived metrics included daily wear time, total activity counts (TAC), ST, light-intensity PA (LPA), moderate-intensity PA (MPA), vigorous-intensity PA (VPA), moderate-to-vigorous PA (MVPA), moderate-to-vigorous PA (MVPA) bouts ≥ 10 minutes (MVPA-10), and proportion of VPA within MVPA (%VPA). Linear mixed models were performed to examine the relationships of these metrics with daily wear time which was categorized into quarters. RESULTS: Majority of the children (96%) provided at least 3 valid accelerometer wear days (defined as ≥ 480 minutes of valid wear time per day), with a total of 797 valid wear days included in analyses. After adjustment for age, sex, and body mass index, none of the PA metrics was completely independent on wear time. A linear relationship was found between wear time and four PA metrics including TAC, MPA, LPA, and ST. However, MVPA estimates were comparable between the first (8 to 11 hours per day) and second quarters (11 to 13 hours per day) of wear time (mean ± standard error: 31.4 ± 1.8 vs 35.7 ± 1.8 minutes, NS). Similar results were found for MVPA-10, VPA, and %VPA. CONCLUSIONS: Accelerometer-derived PA metrics were largely dependent on wear time. MVPA minutes seem to be comparable across wear time of 13 hours per day or less. Comparisons of accelerometer-derived PA estimates between studies need to be cautious and taken into account of differences in wear duration.

Issue/problemImplementation of national systems for patients' physical activity (PA) assessment and counselling is a cost-effective strategy recommended in the WHO Global Action Plan for PA 2018-2030. Although Primary Health Care (PHC) professionals are recognized as key agents for PA promotion, challenges remain on how to develop feasible and scalable tools to support them in promoting patients' PA. The Portuguese model could help other countries improve PA assessment and brief counselling in PHC settings. This study aims to describe the tools' development and usage.Description of the problemThe Portuguese Directorate-General of Health developed two evidence-based digital tools to support PA promotion by healthcare professionals: a) PA brief assessment tool; and b) brief counselling tool. The assessment tool was incorporated within the electronic medical health record software ?SClínico? in September 2017. It includes three questions: 1) how many days per week the patient performs any kind of PA (work, commuting or leisure-time); 2) how much time per day; and 3) how many hours per day the patient spends in sedentary behaviours. The PA brief counselling tool is available through the electronic medical prescription software ?PEM? since December 2017 and consists of five inter-related self-explanatory guides that can be delivered to patients (printed or by email), according to their motivation and PA levels. They facilitate person-centered and autonomy-supportive PA counselling, targeting specific behaviour change mediators, and using validated techniques.ResultsFrom September 2017 to December 2021, 159,179 patients had their PA assessed (2235 per 100,000 users of the National Health Service) and, from these, 16133 received PA brief counselling guides (177 per 100000 residents in Portugal, ≥ 15 years old), with a six-fold and three-fold increases, respectively, between 2018 and 2019 (previous to the COVID-19 pandemic). Future actions will address cost-effectiveness of this policy.LessonsThe brief assessment and brief counselling tools were well-accepted and are increasingly being used, with potential for generalized adoption within the Portuguese Health Care System.Main messagesPortugal has taken a decisive action to promote PA using PHC as a priority setting. PA tools usage is increasing considerably, highlighting the importance of making available easy-to-use PA promotion tools.

Healthcare professionals working in primary care are well positioned to provide initial physical activity counseling owing to their frequent patient contact and their role as a trusted source of health information. Unfortunately, despite health benefits and recommendations, physical activity assessment and promotion in primary care settings is not a routine practice. Because there are currently no standardized practices, it is essential to know what practices are currently employed and explore the barriers to their use. The aim of this study is twofold: first, to describe and compare the practices used by primary care healthcare professionals regarding physical activity assessment and promotion among chronic disease patients and second, to explore the barriers to their use. A cross-sectional survey was conducted among primary care healthcare professionals. The questionnaire designed for this project on physical activity assessment and promotion practices was sent electronically to all 619 primary care healthcare professionals in the Mauricie and Centre-du-Quebec regions. Each item was evaluated with a 5-point Likert scale. Descriptive statistics were used to describe sociodemographic characteristics as well as physical activity assessment and promotion practices. Group comparisons were performed according to professionals' roles. Sixty-eight (11%) of the healthcare professionals responded to the questionnaire. Both physical activity assessment and promotion practices were reported at similar levels, with mean scores of 3.79 ± 1.05 and 3.69 ± 0.78, respectively, on a 5-point Likert scale. A few specific tools and methods are used by healthcare professionals to assess and promote physical activity. The main barriers to physical activity assessment and promotion during clinical consultation were patients' perceived lack of interest in physical activity and professionals' lack of knowledge regarding PA guidelines, tools and methods, and contraindications. Overall, primary care healthcare professionals are convinced of the benefits of physical activity in preventing and treating chronic diseases and recognize they have an important role to play. Although committed to promoting physical activity among chronic disease patients, they require continuous training on guidelines, validated tools, and motivational interviews together with clear advice to properly implement physical activity assessment and promotion practices.

Encoding human activities using multimodal wearable sensory data

Physical activity, as a modifiable factor, emerges as a primary intervention strategy for the prevention and management of gestational diabetes (GD). Among women with GD, the association of physical activity during pregnancy with preterm birth remains unclear. To examine the association of accelerometer-derived physical activity metrics and patterns with preterm birth among women with GD. This prospective cohort study recruited pregnant women with GD in Hangzhou, China, from August 2019 to August 2023 as part of the Westlake Precision Birth Cohort study. Statistical analysis was performed between August and November 2023. Wearable accelerometer-derived physical activity metrics and patterns. Measurements of physical activity via wearable accelerometer were performed at a median (IQR) of 25.4 (24.6-26.6) weeks' gestation. Preterm birth was determined through the examination of delivery records. Incident preterm birth was defined as the delivery of infants before completing 37 weeks of gestation. Among the 1427 women meeting the inclusion criteria, the mean (SD) age was 31.3 (3.8) years, and there were 80 cases of preterm birth. An increase in moderate-to-vigorous intensity physical activity (MVPA) and the fraction of physical activity energy expenditure derived from MVPA exhibited an inverse association with preterm birth, with an odds ratio per 30 minutes of 0.64 (95% CI, 0.42-0.98) and an odds ratio per SD of 0.69 (95% CI, 0.55-0.88). In the dose-response analysis, there was a progressive decrease in the odds of preterm birth with increasing duration of MVPA per day, reaching a plateau at approximately 74 minutes per day. Furthermore, the findings indicated that active MVPA (MVPA ≥30 minutes per day), whether it was concentrated into a few days or followed a more regular pattern, had similar beneficial association with preterm birth. In this prospective cohort study, MVPA during pregnancy exhibited an inverse association with preterm birth among women with GD. Concentrated physical activity was associated with similar benefits in reducing preterm birth risk as regular physical activity.

Background: Physical activity and sleep are two of the Life’s Essential 8 steps for a healthy lifestyle and prevention of cardiovascular disease. The role of weather on an individual’s ability to meet physical activity and sleep goals is unknown. This is relevant in rural settings where the risk of cardiovascular disease is disproportionately high and extreme weather patterns are common. Objective: To investigate the association between local weather conditions and physical activity and sleep, as measured by Fitbit devices among participants in rural Alabama. Methods: Participants were enrolled from rural counties in Alabama as part of an observational study to understand cardiovascular risk in the rural Southeast. Each enrolled participant was given a Fitbit device to collect physical activity and sleep data, including total steps per day and sleep duration for six months. Weather data were obtained from a NOAA Regional Climate Center’s Applied Climate Information System for the dates each participant collected physical activity and sleep data using the participants’ zip code as location. A linear mixed effects model was used to examine the associations of daily precipitation, minimum and maximum daily ambient temperature (modeled as tertiles), BMI, gender, and age with sleep duration and daily physical activity. Results: A total of 942 participants were enrolled in the subcohort from the Alabama counties and 716 (76%) received a Fitbit device. Complete data were available from 561 participants generating 63,176 study-days. The subcohort average age was 48.8 with average BMI of 36.0 Kg/m 2 , 70% (n =660) female, and 78.7% (n = 751) Black. On average, women slept 29.1 more minutes every night (p&lt; 0.001) but recorded 1,225 fewer steps (p &lt;0.001) in comparison to men. An increase in BMI (1 Kg/m 2 increment) was associated with lesser sleep duration (0.98 minutes; p = 0.02) and lower daily step count (70.5, p &lt;0.001). After adjusting for confounders, the upper tertile of ambient daily maximum temperature was associated with lesser sleep duration (5.3 minutes, p&lt;0.001) and physical activity (218 steps, p &lt; 0.001) in comparison to the middle tertile (referent). Conversely lowest tertile of ambient daily maximum temperature was associated with longer sleep duration (5.7 minutes, p=0.003) but not physical activity (22.0 steps, p = 0.71). An increase of an inch of ambient daily precipitation was not significantly associated with sleep duration (p=.49) but was associated with decreased physical activity (251 steps p &lt;0.001). Conclusions: In our cross-sectional study, local temperature and precipitation were associated with lesser sleep duration and physical activity. Further analyses are warranted to understand patterns of these associations and the identification of factors that exacerbate or mitigate these weather-related associations may inform clinical practices and research direction.

Prognostic health management (PHM) systems are designed to predict impending faults and to determine remaining useful life of machinery. An efficient prognostic system can speed up fault diagnosis by providing an indication of what parts of the machinery or vehicle are most likely to fail and will need maintenance in the near future. In this paper, we discuss the essential steps involved in building an effective PHM system. We describe time and frequency domain features that can be extracted from raw sensor data. These features or condition indicators can help summarize the information in raw data and extract critical clues that reflect the health of the machinery. Analytical models can then be used to learn the essential health indicators and how they relate to fault conditions. In addition, we describe a case study of implementing a PHM system for a high speed face milling CNC cutter. We describe features that were analyzed from sensor data. For the analytical engine, we used a Neural Network model for learning the association of the extracted features and the magnitude of wear in the cutter. The neural network was able to determine remaining useful life of cutters in terms of number of remaining cuts for a given wear limit based on extracted features.

Human activity recognition with smartphone sensors using deep learning neural networks

Longitudinal variations in maximal oxygen intake with age and activity.