The Temporal Relationship Between Ecological Pain and Life-Space Mobility in Older Adults With Knee Osteoarthritis: A Smartwatch-Based Demonstration Study

The integration of geographic methods and ecological momentary assessment in public health research: A systematic review of methods and applications

With the widespread prevalence of mobile devices, ecological momentary assessment (EMA) can be combined with geospatial data acquired through geographic techniques like global positioning system (GPS) and geographic information system. This technique enables the consideration of individuals' health and behavior outcomes of momentary exposures in spatial contexts, mostly referred to as "geographic ecological momentary assessment" or "geographically explicit EMA" (GEMA). However, the definition, scope, methods, and applications of GEMA remain unclear and unconsolidated. To fill this research gap, we conducted a systematic review to synthesize the methodological insights, identify common research interests and applications, and furnish recommendations for future GEMA studies. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines to systematically search peer-reviewed studies from six electronic databases in 2022. Screening and eligibility were conducted following inclusion criteria. The risk of bias assessment was performed, and narrative synthesis was presented for all studies. From the initial search of 957 publications, we identified 47 articles included in the review. In public health, GEMA was utilized to measure various outcomes, such as psychological health, physical and physiological health, substance use, social behavior, and physical activity. GEMA serves multiple research purposes: 1) enabling location-based EMA sampling, 2) quantifying participants' mobility patterns, 3) deriving exposure variables, 4) describing spatial patterns of outcome variables, and 5) performing data linkage or triangulation. GEMA has advanced traditional EMA sampling strategies and enabled location-based sampling by detecting location changes and specified geofences. Furthermore, advances in mobile technology have prompted considerations of additional sensor-based data in GEMA. Our results highlight the efficacy and feasibility of GEMA in public health research. Finally, we discuss sampling strategy, data privacy and confidentiality, measurement validity, mobile applications and technologies, and GPS accuracy and missing data in the context of current and future public health research that uses GEMA.

Clinical Assessment of Osteoarthritis Pain: Contemporary Scenario, Challenges, and Future Perspectives

The multifaceted nature of osteoarthritis (OA) pain presents a challenge in understanding and managing the condition. The diverse pain experiences, progression rates, individual responses to treatments, and complex disease mechanisms contribute to heterogeneity in the clinical studies outcomes. The lack of a standardized methodology for assessing and classifying OA pain challenges healthcare practitioners. This complicates the establishment of universally applicable protocols or standardized guidelines for treatment. This article explores the heterogeneity observed in clinical studies evaluating OA pain treatments, highlighting the necessity for refined methodologies, personalized patient categorization, and consistent outcome measures. It discusses the role of the multidimensional nature of OA pain, underlying pain mechanisms, and other contributing factors to the heterogeneity in outcome measures. Addressing these variations is crucial to establishing a more consistent framework for evidence-based treatments and advancing care of the patient with OA pain.

Verification of a comprehensive framework for mobility using data from the Canadian Longitudinal Study on Aging: a structural equation modeling analysis

BACKGROUND

Mobility within and between life spaces is fundamental for health and well-being. Our objective was to verify a comprehensive framework for mobility.

METHODS

This was a cross-sectional study. We used structural equation modeling to estimate associations between latent factors with data from the Canadian Longitudinal Study on Aging for participants 65-85 years of age (65+, n = 11,667) and for adults with osteoarthritis (OA) aged 45-85 (n = 5,560). Latent factors included life space mobility, and physical, psychosocial, environmental, financial, and cognitive elements. Personal variables (age, sex, education) were covariates.

RESULTS

The models demonstrated good fit (65+: CFI = 0.90, RMSEA (90% CI) = 0.025 (0.024, 0.026); OA: CFI = 0.90, RMSEA (90% CI) = 0.032 (0.031, 0.033)). In both models, better psychosocial and physical health, and being less afraid to walk after dark (observed environmental variable) were associated with greater life space mobility. Greater financial status was associated with better psychosocial and physical health. Higher education was related to better cognition and finances. Older age was associated with lower financial status, cognition, and physical health. Cognitive health was positively associated with greater mobility only in the 65 + model. Models generated were equivalent for males and females.

CONCLUSIONS

Associations between determinants described in the mobility framework were verified with adults 65-85 years of age and in an OA group when all factors were considered together using SEM. These results have implications for clinicians and researchers in terms of important outcomes when assessing life space mobility; findings support interdisciplinary analyses that include evaluation of cognition, depression, anxiety, environmental factors, and community engagement, as well as physical and financial health. Public policies that influence older adults and their abilities to access communities beyond their homes need to reflect the complexity of factors that influence life space mobility at both individual and societal levels.

Compliance with ecological momentary assessment programmes in the elderly: a systematic review and meta-analysis

OBJECTIVE

Ecological momentary assessment (EMA) refers to the repeated sampling of information about an individual's symptoms and behaviours, enabling the capture of ecologically meaningful real-time information in a timely manner. Compliance with EMA is critical in determining the validity of an assessment. However, there is limited evidence related to how the elderly comply with EMA programmes or the factors that are associated with compliance.

DESIGN

Systematic review and meta-analysis.

DATA SOURCES

PubMed, Embase, the Cochrane Library and Web of Science were searched up to 17 July 2022.

ELIGIBILITY CRITERIA

We included observational studies on EMA in the elderly reported in English.

DATA EXTRACTION AND SYNTHESIS

Two investigators independently performed screening and data extraction. Discrepancies were resolved by discussion or a third investigator. A systematic review was carried out to characterise the basic characteristics of the participants and EMA programmes. Random-effects meta-analysis was conducted to assess overall compliance and to explore factors associated with differences in compliance among the elderly.

RESULTS

A total of 20 studies with 2047 participants were included in the systematic review and meta-analysis. Meta-analysis showed that the combined compliance rate was 86.41% (95% CI: 77.38% to 92.20%; I²=96.4%; p<0.001). Subgroup analysis revealed high levels of heterogeneity in terms of the methods used to assess population classification, assessment method and assessment frequency, although these may not be the sources of heterogeneity. Meta-regression analysis showed that population classification and assessment period might have a significant impact on heterogeneity (p<0.05). Egger's test indicated significant publication bias (p<0.001).

CONCLUSIONS

Compliance with EMA programmes is high in the elderly. It is recommended that scholars design reasonable EMA programmes according to the health status of the elderly in the future.

Feasibility of a Smartwatch Platform to Assess Ecological Mobility: Real-Time Online Assessment and Mobility Monitor

BACKGROUND

Early detection of mobility decline is critical to prevent subsequent reductions in quality of life, disability, and mortality. However, traditional approaches to mobility assessment are limited in their ability to capture daily fluctuations that align with sporadic health events. We aim to describe findings from a pilot study of our Real-time Online Assessment and Mobility Monitor (ROAMM) smartwatch application, which uniquely captures multiple streams of data in real time in ecological settings.

METHODS

Data come from a sample of 31 participants (Mage = 74.7, 51.6% female) who used ROAMM for approximately 2 weeks. We describe the usability and feasibility of ROAMM, summarize prompt data using descriptive metrics, and compare prompt data with traditional survey-based questionnaires or other established measures.

RESULTS

Participants were satisfied with ROAMM's function (87.1%) and ranked the usability as "above average." Most were highly engaged (average adjusted compliance = 70.7%) and the majority reported being "likely" to enroll in a 2-year study (77.4%). Some smartwatch features were correlated with their respective traditional measurements (eg, certain GPS-derived life-space mobility features (r = 0.50-0.51, p < .05) and ecologically measured pain (r = 0.72, p = .01), but others were not (eg, ecologically measured fatigue).

CONCLUSIONS

ROAMM was usable, acceptable, and effective at measuring mobility and risk factors for mobility decline in our pilot sample. Additional work with a larger and more diverse sample is necessary to confirm associations between smartwatch-measured features and traditional measures. By monitoring multiple data streams simultaneously in ecological settings, this technology could uniquely contribute to the evolution of mobility measurement and risk factors for mobility loss.

Comparisons Between GPS-based and Self-reported Life-space Mobility in Older Adults

Assessments of Life-space Mobility (LSM) evaluate the locations of movement and their frequency over a period of time to understand mobility patterns. Advancements in and miniaturization of GPS sensors in mobile devices like smartwatches could facilitate objective and high-resolution assessment of life-space mobility. The purpose of this study was to compare self-reported measures to GPS-based LSM extracted from 27 participants (44.4% female, aged 65+ years) who wore a smartwatch for 1-2 weeks at two different site locations (Connecticut and Florida). GPS features (e.g., excursion size/span) were compared to self-reported LSM with and without an indicator for needing assistance. Although correlations between self-reported measures and GPS-based LSM were positive, none were statistically significant. The correlations improved slightly when needing assistance was included, but statistical significance was achieved only for excursion size (r=0.40, P=0.04). The poor correlations between GPS-based and self-reported indicators suggest that they capture different dimensions of life-space mobility.

Exploiting real-world data to monitor physical activity in patients with osteoarthritis: the opportunity of digital epidemiology

Osteoarthritis is a degenerative joint disease that affects millions of people worldwide. Current guidelines emphasize the importance of regular physical activity as a preventive measure against disease progression and as a valuable strategy for pain and functionality management. Despite this, most patients with osteoarthritis are inactive. Modern technological advances have led to the implementation of digital devices, such as wearables and smartphones, showing new opportunities for healthcare professionals and researchers to monitor physical activity and therefore engage patients in daily exercising. Additionally, digital devices have emerged as a promising tool for improving frequent health data collection, disease monitoring, and supporting public health surveillance. The leveraging of digital data has laid the foundation for developing a new concept of epidemiological study, known as "Digital Epidemiology". Analyzing real-world data can change the way we observe human behavior and suggest health interventions, as in the case of physical exercise and osteoarthritic patients. Furthermore, large-scale data could contribute to personalized and precision medicine in the future. Herein, an overview of recent clinical applications of wearables for monitoring physical activity in patients with osteoarthritis and the benefits of exploiting real-world data in the context of digital epidemiology are discussed.

Discovering Knee Osteoarthritis Imaging Features for Diagnosis and Prognosis: Review of Manual Imaging Grading and Machine Learning Approaches

Knee osteoarthritis (OA) is a deliberating joint disorder characterized by cartilage loss that can be captured by imaging modalities and translated into imaging features. Observing imaging features is a well-known objective assessment for knee OA disorder. However, the variety of imaging features is rarely discussed. This study reviews knee OA imaging features with respect to different imaging modalities for traditional OA diagnosis and updates recent image-based machine learning approaches for knee OA diagnosis and prognosis. Although most studies recognized X-ray as standard imaging option for knee OA diagnosis, the imaging features are limited to bony changes and less sensitive to short-term OA changes. Researchers have recommended the usage of MRI to study the hidden OA-related radiomic features in soft tissues and bony structures. Furthermore, ultrasound imaging features should be explored to make it more feasible for point-of-care diagnosis. Traditional knee OA diagnosis mainly relies on manual interpretation of medical images based on the Kellgren-Lawrence (KL) grading scheme, but this approach is consistently prone to human resource and time constraints and less effective for OA prevention. Recent studies revealed the capability of machine learning approaches in automating knee OA diagnosis and prognosis, through three major tasks: knee joint localization (detection and segmentation), classification of OA severity, and prediction of disease progression. AI-aided diagnostic models improved the quality of knee OA diagnosis significantly in terms of time taken, reproducibility, and accuracy. Prognostic ability was demonstrated by several prediction models in terms of estimating possible OA onset, OA deterioration, progressive pain, progressive structural change, progressive structural change with pain, and time to total knee replacement (TKR) incidence. Despite research gaps, machine learning techniques still manifest huge potential to work on demanding tasks such as early knee OA detection and estimation of future disease events, as well as fundamental tasks such as discovering the new imaging features and establishment of novel OA status measure. Continuous machine learning model enhancement may favour the discovery of new OA treatment in future.

Dynamics of Loneliness Among Older Adults During the COVID-19 Pandemic: Pilot Study of Ecological Momentary Assessment With Network Analysis

OBJECTIVE

The COVID-19 pandemic has had potentially severe psychological implications for older adults, including those in retirement communities, due to restricted social interactions, but the day-to-day experience of loneliness has received limited study. We sought to investigate sequential association, if any, between loneliness, activity, and affect.

METHODS

We used ecological momentary assessment (EMA) with dynamic network analysis to investigate the affective and behavioral concomitants of loneliness in 22 residents of an independent living sector of a continuing care retirement community (mean age 80.2; range 68-93 years).

RESULTS

Participants completed mean 83.9% of EMA surveys (SD = 16.1%). EMA ratings of loneliness were moderately correlated with UCLA loneliness scale scores. Network models showed that loneliness was contemporaneously associated with negative affect (worried, anxious, restless, irritable). Negative (but not happy or positive) mood tended to be followed by loneliness and then by exercise or outdoor physical activity. Negative affect had significant and high inertia (stability).

CONCLUSIONS

The data suggest that EMA is feasible and acceptable to older adults. EMA-assessed loneliness was moderately associated with scale-assessed loneliness. Network models in these independent living older adults indicated strong links between negative affect and loneliness, but feelings of loneliness were followed by outdoor activity, suggesting adaptive behavior among relatively healthy adults.

Applications of Wearable Technology in a Real-Life Setting in People with Knee Osteoarthritis: A Systematic Scoping Review

With the growing number of people affected by osteoarthritis, wearable technology may enable the provision of care outside a traditional clinical setting and thus transform how healthcare is delivered for this patient group. Here, we mapped the available empirical evidence on the utilization of wearable technology in a real-world setting in people with knee osteoarthritis. From an analysis of 68 studies, we found that the use of accelerometers for physical activity assessment is the most prevalent mode of use of wearable technology in this population. We identify low technical complexity and cost, ability to connect with a healthcare professional, and consistency in the analysis of the data as the most critical facilitators for the feasibility of using wearable technology in a real-world setting. To fully realize the clinical potential of wearable technology for people with knee osteoarthritis, this review highlights the need for more research employing wearables for information sharing and treatment, increased inter-study consistency through standardization and improved reporting, and increased representation of vulnerable populations.

Wearable electronic devices for chronic pain intensity assessment: A systematic review

Wearable electronic devices are a convenient solution to pain intensity assessment as they can provide continuous monitoring for more precise medication adjustments. However, there is little evidence regarding the use of wearable electronic devices for chronic pain intensity assessment. Our primary objective was to examine the physiologic parameters used by wearable electronic devices for chronic pain intensity assessment. We initially inquired PubMed, CINAHL, and Embase for studies evaluating the use of wearable electronic devices for chronic pain intensity assessment. We updated our inquiry by searching on PubMed, Embase, Scopus, and Google Scholar. English peer-reviewed studies were included, with no exclusions based on time frame or publication status. Of 348 articles that were identified on the first inquiry, 8 fulfilled the eligibility criteria. Of 179 articles that were identified on the last inquiry, only 1 fulfilled the eligibility criteria. We found articles evaluating wristbands, smartwatches, and belts. Parameters evaluated were psychomotor and sleep patterns, space and time mobility, heart rate variability, and skeletal muscle electrical activity. Most of the studies found significant positive associations between physiological parameters measured by wearable electronic devices and self-reporting pain scales. Wearable electronic devices reliably reflect physiologic or biometric parameters, providing a physiological correlation for pain. Early stage investigation suggests that the degree of pain intensity can be discerned, which ideally will reduce the bias inherent to existing numeric/verbal scales. Further research on the use of these devices is vital.

A Space-Time Analysis of Rural Older People's Outdoor Mobility and Its Impact on Self-Rated Health: Evidence from a Taiwanese Rural Village

With the aggravation of rural aging, the well-being and self-rated health level of older people in rural communities are significantly lower than those in urban communities. Past studies hold that mobility is essential to the quality of life of the elderly, and well-being depends on their own adaptation strategies in the built environment. Therefore, this study combines three key factors related to active aging: environment, health and mobility, and assumes that the elderly with good health status will have environmental proactivity and a wider range of daily mobility in a poor rural built environment. This study attempts to track daily mobility by using a space-time path method in time geography and then to explore the relationship between outdoor mobility and older people's self-rated health. A 1-week mobility path survey for 20 senior citizens of Xishi Village, a typical rural village in Taiwan, was conducted by wearing a GPS sports watch. A questionnaire survey and in-depth interviews were done to provide more information about the seniors' personal backgrounds and lifestyles. The results show that when the built environment is unfit to the needs of daily activities, half of the participants can make adjustment strategies to go beyond the neighborhoods defined by administrative units. Correlation analysis demonstrated that mental health is associated with daily moving time and distance. In addition, men have higher self-rated health scores than women, and there are significant statistical differences between married and widowed seniors in daily outing time and distance. This exploratory study suggests that in future research on rural health and active aging in rural areas, understanding the daily outdoor mobility of the elderly can help to assess their health status and living demands and quickly find out whether there is a lack of rural living services or environmental planning.