Sage Journals: Discover world-class research

Abstract

Background

In recent years, the number of individuals with knee osteoarthritis (KOA) receiving home-based conservative management has increased. These individuals often experience multiple symptoms that impair daily functioning, highlighting the need for remote monitoring. Ecological momentary assessment (EMA) enables repeated, real-time monitoring of symptoms, activities, and environmental factors in natural settings. However, specific scenarios for applying EMA to assess KOA-related symptoms, together with acceptability and operational feasibility among patients in clinical practice, lack systematic evidence; therefore, the practical value of EMA requires further clarification.

Objective

This study aims to systematically synthesize evidence on the feasibility and clinical value of EMA for evaluating and dynamically monitoring symptoms in patients with KOA. Based on this synthesis, the challenges, emerging opportunities, and future research directions for EMA applications in KOA are further identified.

Methods

Following the Arksey and O'Malley framework and the PRISMA-ScR checklist, nine databases were systematically searched from inception to June 2025. After the screening process, relevant data were extracted from all studies that met the inclusion criteria.

Results

A total of 24 studies were included. This review systematically summarized and analyzed study design characteristics, EMA data collection methods, response-related outcomes, and the main findings. Several gaps were identified. Sample size, follow-up duration, and data collection frequency varied substantially across studies. In addition, all included studies were limited to the assessment phase, with no translation of EMA use into actual symptom management for patients.

Conclusion

EMA is a feasible and valuable approach for evaluating symptoms in patients with KOA. Future studies should establish standardized data collection standards, optimize data collection tools, and further integrate EMA into symptom intervention and management to advance personalized, precision-based care.

Keywords

knee osteoarthritis ecological momentary assessment symptoms monitoring scoping review

1. Introduction

The number of OA cases worldwide has reached 595 million, accounting for approximately 7.6% of the global population.¹ KOA is the most common subtype among the various forms of OA. As of 2019, an estimated 528 million individuals worldwide were affected.² People with KOA often present with comorbid symptoms such as pain, sleep disturbances, fatigue, and anxiety.^3–5 These symptoms significantly impair patients’ daily activities, reduce their occupational productivity,⁶ and impose substantial burdens on families and society.⁷ People with KOA typically opt for either joint replacement or conservative treatment. In recent years, the number of patients undergoing surgical treatment has been increasing; however, owing to disease severity, age, educational level, and other factors, a subset of individuals still choose conservative treatment.^8,9 For people who are receiving conservative treatment or who do not meet surgical indications, home-based care is more common; thus, long-term assessment, dynamic monitoring, and timely intervention of their various symptoms in daily life are particularly important.

Disease progression and symptom experience exhibit distinct temporal characteristics,^10,11 making longitudinal research on symptom-related factors valuable for guiding nursing interventions. Consequently, many researchers have actively assessed the long-term monitoring of KOA-related symptoms and their associations with various influencing factors.^5,12 However, data collection in these studies predominantly relies on retrospective questionnaires. These methods have limitations in accurately capturing real-time data, fail to reflect the dynamic evolution of symptoms and are prone to introducing recall bias, compromising the reliability of the results.^13,14

The concept of EMA was first introduced by Stone et al.¹⁵ in 1994; they defined it as the repeated sampling of individuals’ symptoms, behaviors, affect, or cognition via portable devices. The EMA prompts participants at random or predefined time points within a specific period to report their current status, thereby capturing the dynamic changes in experiences in real-world contexts.¹⁶ This measurement approach is characterized by the following features: real-time data collection in the natural environments of individuals, which minimizes recall bias associated with retrospective reports and ensures more objective and accurate recording and repeated sampling over short intervals, allowing the capture of moment-to-moment fluctuations rather than relying on single-point retrospective assessments.¹⁷

Some researchers have recognized the advantages of using the EMA to assess various symptoms in patients with KOA. Existing studies have used EMA to monitor data in patients with KOA, identifying relevant symptoms overlooked by conventional assessment methods.¹⁸ Additionally, other studies have noted that the EMA’s data collection approach enables timely information recording to prevent omissions, thereby increasing data accuracy.¹⁹ Smith et al.²⁰ used EMA and reported that fatigue significantly affected physical activity levels in older adults with KOA. Similarly, Heijman et al.²¹ applied EMA and reported considerable individual variability in fatigue fluctuations, which were significantly associated with pain and emotional states.

Although ecological momentary assessment has been increasingly adopted to evaluate symptoms in individuals with knee osteoarthritis, the overall scope and key characteristics of its application in this population have not been fully established. In particular, the implementation of ecological momentary assessments across studies, including assessment settings, monitoring periods, sampling frequency, and outcome measures, has not been systematically reviewed, and its feasibility, practical utility, and potential value for symptom management remain poorly understood. Accordingly, this scoping review was performed to systematically identify and map the literature on the use of ecological momentary assessment for symptom assessment and management in patients with knee osteoarthritis. Three specific research questions were addressed: (1) To synthesize current evidence on the application characteristics of the EMA for symptom evaluation in patients with KOA. (2) To analyze the data collection devices, approaches, and key procedures employed in its use. (3) To evaluate the current status, feasibility, and utility of the EMA in this context. This study has been registered with the Open Science Framework under the registration number https://doi.org/10.17605/OSF.IO/GFQMD.

2. Methods

This scoping review was guided by the framework developed by Arksey and O'Malley,²² as well as the subsequent enhancements proposed to strengthen the methodology.²³ This review followed five key stages: identifying the research question; identifying relevant studies; selecting studies; charting the data; and collating, summarizing, and reporting the results.²² The results are reported according to the PRISMA extension for scoping reviews (PRISMA-ScR) guidelines,²⁴ the completed checklist of which is provided in Appendix 1.

2.1. Identifying the research questions

The specific research questions addressed include the following:

(1) What is the utility of EMA in people with KOA? (2) What are the methods used for collecting EMA data? What are the key procedures and main contents involved in EMA data collection? (3) How feasible is the application of EMA in patients with KOA? (4) What challenges have been reported in the practical application of EMA in patients with KOA?

2.2. Search strategy

An initial exploratory search was conducted to refine the search terms and clarify the inclusion and exclusion criteria. A comprehensive literature search was conducted across 9 databases: PubMed, Web of Science, Embase, ProQuest, CINAHL, the Cochrane Library, the China Biology Medicine disc, the China National Knowledge Infrastructure, and Wanfang Data. A research librarian and members of the research team collaboratively developed the search terms and strategies. These included subject-related terms (e.g., osteoarthritis knee and knee osteoarthritis) and intervention-related terms (e.g., ecological momentary assessment, experience samples, real-time assessment, and daily samples). We used a search strategy that combined controlled vocabulary and free-text terms and applied citation snowballing by screening the reference lists of retrieved studies to identify additional eligible records. The search period for each database ranged from inception to June 2025. The search strategies for all the databases are provided in Appendix 2.

2.3. Eligibility criteria

The inclusion criteria were developed based on the PCC framework (participants, concept, and context). Participants (P): Patients who were clinically diagnosed with unilateral or bilateral KOA or individuals with symptoms related to KOA. There were no restrictions regarding age or sex. Concept (C): The studies needed to involve patients’ active use of EMA for monitoring relevant indicators. All the data were self-reported by the participants through either paper-based diaries or electronic devices, and key outcome measures were recorded multiple times daily over a period exceeding 24 h. Context (C): People with KOA use the EMA for symptom assessment and monitoring in daily life, as well as for its feasibility and utility.

Studies were excluded if they met any of the following conditions:

(1) Studies did not report the implementation or outcomes of the EMA. (2) Studies that were not published in English or Chinese. (3) A study in which data were collected only once daily using a recall-based method. (4) Studies were systematic reviews, meta-analyses, case reports, or conference abstracts or whose full texts were not available. (5) The study was conducted in a laboratory setting and not in a real-world context.

2.4. Study selection

All the retrieved records from the nine databases were imported into EndNote 20 for deduplication. Two evidence-based reviewers independently screened the titles and abstracts against the inclusion and exclusion criteria. The same two reviewers reviewed the full texts of potentially eligible articles jointly to determine final inclusion, and the reasons for exclusion were recorded. Any disagreements during the selection process were resolved through discussion with a third reviewer or supervisor until a consensus was reached.

2.5. Quality assessment

Consistent with the scoping review methodology, we did not perform a formal critical appraisal or risk-of-bias assessment of the included studies because this scoping review aimed to examine the current state of research on EMA use in patients with KOA and to identify research gaps rather than to estimate intervention effects or provide a graded assessment of evidence strength.^25,26 This approach is also aligned with PRISMA-ScR, in which critical appraisal is reported as an optional item when conducted.²² To enhance methodological rigor during the review process, study selection and data charting were conducted independently by two reviewers using predefined eligibility criteria, and inclusion was restricted to peer-reviewed journal articles. During data charting, we identified missing and inconsistent reporting of some EMA-related result data across the included studies, and these reporting issues were taken into account and discussed during synthesis and interpretation. Findings were interpreted within study design categories to support cautious and context-appropriate interpretation, particularly when feasibility, acceptability, and implementation implications were discussed. However, these considerations were used to inform interpretation rather than to assign formal quality scores or determine study eligibility. Accordingly, although no formal quality scores were assigned, differences in study design, reporting completeness, and methodological heterogeneity were taken into account when the findings were interpreted across all included studies.

2.6. Data extraction and analysis

The data were extracted independently by two reviewers using a standardized electronic spreadsheet. The extracted data included the following:

(1) Study characteristics, such as research objectives, sample characteristics (sample size), year of publication, country of investigation, and outcome variables assessed. (2) EMA-related implementation details, including the device used, data collection method (application and input formats), monitoring frequency, duration, and sampling method. (3) EMA compliance outcomes, such as the participation rate and response rate. Any disagreements between the two reviewers were discussed and resolved by a third reviewer.

3. Results

A total of 1,573 records were initially retrieved from the databases. After removing duplicates, 1,256 records were retained. Two reviewers independently screened the titles and abstracts, excluding 1,188 records. The full texts of the remaining 68 articles were assessed, and 18 were excluded based on the eligibility criteria. Six more studies were identified through snowballing by reviewing the reference lists of relevant articles, and 24 studies were ultimately included in this review (Figure 1).

Figure 1.

A PRISMA flowchart.

3.1. Characteristics of the included studies

These studies were published between 2002 and 2025, with the highest proportion (37.5%) published in the past five years. The 24 studies were conducted in five countries, namely the United States (n = 18), New Zealand (n = 3), the United Kingdom (n = 1), the Netherlands (n = 1), and Denmark (n = 1). Most studies (75%) were conducted in the United States. The characteristics of the included studies included research objectives, study designs, study samples, and characteristics of the study outcomes. Table 1 presents an overview of the general characteristics of the included studies. The detailed study characteristics of the included articles are provided in Appendix 3.

Table 1.

Overview of the selected studies.

Study characteristics	Studies, n (%)	References
Study location
United States	18 (75%)	18,20,27–42
New Zealand	3 (13%)	19,43,44
United Kingdom	1 (4%)	45
Netherlands	1 (4%)	21
Denmark	1 (4%)	46
Study design
Observational study	20 (84%)	20,21,28–46
Interventional study	2 (8%)	27
Mixed-methods study	1 (4%)	18
Qualitative study	1 (4%)	19
Sample size
<50	11 (46%)	18,19,21,27–29,38,39,41,45,46
50∼100	4 (17%)	32,37,43,44
100∼150	2 (8%)	30,36,42
150∼200	1 (4%)	40
>200	6 (25%)	20,31,33–35
Data collection Device
Paper diary	7 (29%)	18,28,29,37,40,41,45
Telephone	7 (29%)	20,33–35,37,41,42
PDA	4 (17%)	27,30,31,36
Smartphone	6 (25%)	19,21,39,43,44,46
Wearable devices	12 (50%)	18,20,28,30–32,36,38–40,45,46
Outcomes of EMA monitoring
Pain	6 (25%)	18,40,41,43,44,46
Physical activity	5 (21%)	20,28,30,31,36
Fatigue	5 (21%)	21,32,40,42,45
Mood	5 (21%)	29,33–35,42
Others	5 (21%)	19,27,37–39

3.1.1. Summary of study objectives

The objectives of the included studies focused primarily on the following three aspects:

(1) KOA patients’ authentic symptom experiences, person-level heterogeneity, and same-day fluctuation patterns of multiple symptoms were investigated.^18,41,42 (2) The influencing factors and correlations among various symptoms in KOA patients,^{20,21,28–37,39,40,43–46} including the interrelationships among pain, fatigue, and physical activity and their associated factors were investigated. (3) Feasibility analysis of the application of EMA to assess symptoms,^19,27,38 such as patient experiences and perceptions related to the use of EMA tools and devices, in patients with KOA.

3.1.2. Characteristics of study design

Most of the included studies were observational and used EMA to collect and analyze data on symptoms, behaviors, or other relevant variables in patients with KOA. One study¹⁸ employed a mixed-methods approach, combining quantitative data collection with qualitative insights. One study¹⁹ adopted a qualitative research design, using interviews to assess patients’ experiences and perceptions of the application of EMA and the feasibility of EMA-based data collection. Among the included studies, only one study²⁷ was the only study that employed an interventional design, evaluating the effects of electronic versus paper diary recording.

3.1.3. Characteristics of the study samples

The included studies ranged in sample size from 17 to 326 participants; the mean sample size was 104. After two studies that did not report participants’ mean age were excluded, the remaining studies reported a mean age of 65 years. One study³¹ included not only patients with KOA but also their respective spouses for data collection. Three studies^29,37,41 focused specifically on obese patients with KOA. Murphy et al.³² included only female patients with KOA.

3.1.4. Characteristics of the study outcomes

EMA can be used to assess various symptoms in KOA patients. The primary outcome measures included symptoms such as pain, fatigue, and emotion. In total, 6 studies^{18,40,41,43,44,46} used pain as the primary outcome measure and examined its relationships with factors such as activity,^18,41,46 mood,⁴⁴ sleep,⁴⁰ and an individual’s sensitivity to pain.⁴³ Five studies^{21,32,40,42,45} used fatigue as the primary outcome measure and determined its correlations with physical activity, affect, sleep, and pain. Four studies^29,33–35 focused primarily on patients’ emotional symptoms. Five studies^{20,28,30,31,36} used physical function and mobility as their primary outcomes in individuals with KOA.

3.2. EMA data collection characteristics

The data collection characteristics include the data collection devices, data input methods, data collection periods, data collection frequency, and study participants’ participation and response rates during the collection process.

3.2.1. Data collection devices and input modes

As shown in Figure 2, the use of smartphones and wearable devices for data collection in EMA has increased steadily over time. Among the included studies, data were collected most commonly using electronic devices. A total of 12 studies^{18,20,28,30–32,36,38–40,45,46} utilized wearable devices, making them the most prevalent tool, followed by paper diaries^{18,28,29,37,40,41,45} and telephones.^{20,33–35,37,41,42} Smartphones and PDAs were employed in six^{19,21,39,43,44,46} and four^27,30,31,36 studies, respectively. Some studies have adopted a combined approach using multiple data collection tools.^{18,20,28,30,31,36,37,39–41,45,46} Paper diaries have been consistently used across different time periods, and in the past five years, they have typically been employed in conjunction with other data collection devices.

Figure 2.

The evolution of data collection modalities and publication volume in EMA research (2000–2025).

3.2.2. Duration and frequency of data collection

The duration of EMA monitoring varied considerably across the included studies, ranging from 2 days to 22 days. The most common monitoring durations were seven days (n = 7),^{18,20,28,33,34,42,45} 14 days (n = 7)^{19,21,27,35,38,43,44} and 22 days (n = 3).^30,31,36 In a study by Trudeau et al.,²⁷ participants underwent two separate 7-day EMA monitoring periods during the first and second treatment phases. The study by Behrens et al.³⁵ was conducted at two time points: baseline and at the 1-year follow-up. Across the included studies, the EMA prompt frequency ranged from once to 33 times per day. In 9 studies,^{19,30,31,36,38,39,43–45} participants were required to complete three assessments per day. In two studies by Focht et al.^29,41 participants were instructed to complete five prompts on exercise days and six prompts on non-exercise days. Choi et al.³⁷ implemented an intensive sampling protocol requiring 2–3 entries per hour and self-reports following each eating episode.

3.2.3. Response and compliance rates

This section summarizes participant engagement in EMA protocols, including the participation rate, response rate, and use of reminder and incentive strategies. All the included studies except two reported^19,35 participant participation rates, ranging from 68% to 100%. Six studies^{18,19,21,27,28,37} did not report EMA-specific response rates. Among the remaining studies, the reported response rates ranged from 78% to 96.9%. Most studies provided training on EMA procedures before data collection to promote participant engagement and compliance. Additionally, system-generated reminders have been implemented in most studies to prompt timely responses. Incentive mechanisms were applied in five studies.^{19,37,39,43,44} In three studies,^19,43,44 participants received USD 100 supermarket vouchers. In the study by Choi et al.,³⁷ participants received monetary compensation: USD 15 for completing a daily diary, with a USD 10 bonus for full compliance throughout the study period. Mardini et al.³⁹ provided participants with a USD 50 gift card as a reward for their participation in the study.

3.3. Main findings

This section focuses on the primary symptoms reported by patients with KOA by the EMA, the interrelationships among these symptoms and other factors, and the feasibility of using the EMA to collect patient-reported information (Figure 3).

Figure 3.

Term co-occurrence network of titles and abstracts of included studies (VOS viewer).

3.3.1. Symptom perception and trajectories

Four studies used EMA to document experiences of pain symptoms and their long-term dynamic characteristics, focusing primarily on “pain flares” (a specific type of pain in daily life) and pain fluctuations. One study¹⁸ indicated that “pain flares” are common manifestations of pain, with considerable variability in pain perception among different participants. One study⁴⁴ reported that patients’ pain does not remain at a stable average level but does exhibit dynamic fluctuations throughout the day and from day to day. Focht et al.⁴¹ reported that pain intensity follows a “rise-then-fall” trajectory: it gradually increases from morning, peaks at 3–4 p.m., and then gradually subsides. A dynamic study examining fatigue revealed that all participants experienced moderate fatigue. However, diurnal patterns of fatigue vary by race: non-Hispanic White participants exhibited a significant increase in fatigue from morning to evening, whereas African American participants presented a more minor, more gradual increase throughout the day.⁴²

3.3.2. Pain

Five studies^{18,39,41,43,46} explored the relationship between physical activity and pain in patients with KOA. Overall, vigorous or high-intensity exercise is frequently associated with transient pain flares, which typically subside by the next day.^18,41 Overton et al.⁴³ reported substantial individual variability: patients who experienced significant pain during activity tended to have poorer and more unstable daily pain patterns. Research²⁸ revealed that higher physical activity levels correlate with increased pain on the same day, whereas another study³⁹ reported that pain, in turn, leads to reductions in range of motion, activity duration, walking distance, and the area of the movement trajectory ellipse.

Evidence suggests that poorer perceived sleep quality at night is also associated with higher pain levels the following morning.⁴⁰ Additionally, there is an association between mood and pain. When negative emotions such as stress, anxiety, and loneliness are more severe than usual, pain intensifies.⁴⁴ Pain also affects emotional well-being, with several moderators shaping this relationship. One study indicated that elevated pain levels lead to a reduction in positive feelings.²⁹ Higher levels of pain predispose individuals to negative emotions, whereas social interaction can buffer this effect.³³ Depressive symptoms amplify the pain-negative affect association, especially among individuals with more severe depression, while having less influence on positive affect.³⁴ Stressful life events in the past year intensified the immediate emotional impact of pain on negative affect, indirectly increasing depressive symptoms, particularly under high-stress conditions.³⁵

3.3.3. Fatigue

Multiple studies have consistently shown a bidirectional relationship between fatigue and physical activity in patients with KOA. High-intensity activity increases the likelihood of fatigue,³² whereas greater fatigue predicts reduced activity levels.^20,45 Both pain and mood are important predictors of fatigue in patients with KOA. Greater pain intensity is consistently associated with greater fatigue,⁴² and lower positive mood or higher negative mood, whether it occurs as short-term fluctuations or as chronic states, is correlated with increased fatigue severity.^21,29 Stress and pain may further mediate the association between mood and fatigue, underscoring the complex interaction between physical and emotional factors in contributing to the fatigue burden.²⁹

3.3.4. Other findings

Studies examining psychological factors and social support in patients with KOA suggest that morning psychological states influence physical activity patterns throughout the day, with greater pain catastrophizing predicting lower activity and greater sedentary behavior³⁰ and greater self-efficacy predicting increased daily activity.³⁶ A study revealed that higher levels of pain are associated with greater concurrent intake of calories and fat.³⁷ A spouse’s timely and empathetic response to a patient’s pain can increase long-term physical function. This effect is observed over 18 months.³¹

3.3.5. Feasibility

Three studies^19,27,38 demonstrated the feasibility of using the EMA to collect patient-reported data in patients with KOA. EMA, particularly when integrated with wearable devices, shows greater responsiveness and accuracy than traditional questionnaires do and identifies information missed by retrospective research methods.²⁷ Compared with paper diaries, smartphone-based entries are more convenient and reduce recall bias, and high compliance rates and the willingness to participate in long-term studies further support the practicality of EMAs.^19,38

3.3.6. Data collection characteristics and adherence

The data in Figure 4 were used to visualize study-level co-occurrence patterns among EMA protocol characteristics and engagement indicators. The protocol characteristics included the data collection modality, monitoring duration, and prompt frequency, whereas the engagement indicators included compliance and response rates. Visual inspection revealed substantial heterogeneity in protocol feature combinations across studies. Several suggestive patterns were observed. More intensive protocols, particularly those with frequent prompting, were sometimes associated with lower compliance. For example, Choi et al.³⁷ used a high-intensity schedule requiring two to three entries per hour and reported a compliance rate of 76%. Longer monitoring periods were also occasionally associated with lower levels of engagement. Mardini et al.³⁹ monitored participants for 13 days and reported 68% compliance and 82% response. However, these patterns were not consistent, as some longer-duration protocols with moderate prompting still demonstrated high compliance. Studies employing the same prompt frequency, such as three prompts per day, have shown considerable variation in compliance and response rates. No clear device-related clustering of engagement outcomes was identified.

Figure 4.

The impact of EMA design features on participant compliance: A Heatmap visualization.

4. Discussion

We systematically retrieved and synthesized literature on the use of EMA to evaluate disease symptoms and related interventions in patients with KOA. This study represents the first synthesis of research examining the use of EMA for assessment in patients with KOA. Participation and response rates among participants were generally favorable. The synthesized findings indicate that EMA is not only feasible and widely accepted but also provides unique value for dynamically monitoring symptoms such as pain and fatigue in patients with KOA, as well as their interactions with other factors.

4.1. Feasibility and utility of EMA in people with KOA

Our review indicates that EMA is a feasible and convenient approach for symptom monitoring in individuals with KOA, with most included studies reporting favorable participant engagement. A plausible explanation is that EMA enables real-time capture of symptoms in daily environments, avoiding repeated clinic visits and paper diaries, thereby minimizing disruption to daily routines and supporting sustainable monitoring. This interpretation is supported by evidence in other clinical populations; for example, a randomized crossover study⁴⁷ in fibromyalgia patients reported that smartphone-based diaries were well accepted and provided more accurate and complete symptom ratings than paper diaries, even among individuals with lower education levels and limited technology experience. Electronic diaries also enhance the temporal accuracy and verifiability of entries, further emphasizing the practical and data-quality advantages of EMA for monitoring symptoms in KOA. Furthermore, our review demonstrates that pain in KOA is dynamically associated with activity intensity and psychological factors such as mood fluctuations, pain catastrophizing, and stress. By repeatedly capturing symptoms in real-world contexts, EMA enables researchers and clinicians to characterize symptom relationships and dynamic interactions over time,⁴⁸ which may help identify core symptom profiles and inform the development of more targeted interventions for KOA management.^49,50

4.2. EMA implementation heterogeneity and study comparability

In the present review, reporting of key EMA characteristics varied substantially across studies, most notably in prompting frequency and response or compliance indicators. This heterogeneity should be considered when interpreting data completeness and response-related outcomes, as inconsistent protocol design and reporting practices restrict direct cross-study comparability. Data collection modality may further influence participant engagement, burden, and data completeness. Participant burden may arise not only from protocol design but also from unfamiliarity with study platforms, technical difficulties, and the use of additional wearable devices. Among older populations, technology-related factors such as device access and usability are associated with compliance,⁵¹ and EMA tools tailored to participant needs may improve acceptability and adherence. Monitoring duration and prompting frequency also influence data completeness and should therefore be considered when interpreting response or compliance rates.⁵² Meta-analytic evidence suggests that EMA studies often balance participant burden by combining higher prompting frequencies with shorter monitoring periods, with a negative association observed between the number of daily prompts and monitoring duration.⁵³ Compliance may decline when monitoring periods extend beyond approximately one week. In this review, participant engagement was generally high, likely due to the frequent use of short monitoring periods spanning seven to 14 days and moderate prompting schedules of approximately three prompts per day. This methodological heterogeneity restricts cross-study comparability and may influence the strength and generalizability of conclusions regarding EMA performance in KOA.⁵⁴

4.3. Recommendations for future research

4.1.1. Standardization

Future EMA research in KOA populations should prioritize methodological standardization and transparency in reporting. Key protocol features should be prespecified and clearly described, including data collection modality, prompt frequency and timing, monitoring duration, sampling scheme, compliance definitions, valid response windows, missing-data handling, and data processing procedures. Monitoring frequency and duration should be determined a priori according to study objectives and anticipated participant burden, rather than increasing sampling intensity without clear justification.⁵⁵ Future studies should also strengthen protocol implementation through user-centered design, standardized participant training, pilot testing, and refinement of protocol intensity and operational procedures before full-scale deployment.⁵⁶ Where appropriate, transparent incentive strategies may also be applied to support participant adherence and improve data completeness. Moreover, consistent reporting of response-related indicators, together with clearly defined denominators and reported reasons for missing data when available, is essential to improve cross-study comparability and facilitate future evidence synthesis in KOA EMA research.

4.1.2. Technology design

Among the studies included in this review, EMA was used primarily as a tool for data collection, and none of the studies applied EMA directly for symptom intervention or management in patients with KOA. However, emerging evidence suggests that EMA can improve the efficiency of data acquisition and also function as a platform for delivering interventions.⁵⁷ Researchers have recently begun exploring the use of EMA in intervention programs for populations with disordered eating.⁵⁸ This type of intervention is referred to as an ecological momentary intervention (EMI). In 2010, Heron and Smyth provided a systematic definition of EMI, describing it as technology-supported treatments delivered in real time within individuals’ natural environments, typically guided by EMA data, to provide context-sensitive and personalized support targeting dynamic psychological and behavioral processes in daily life.⁵⁹ Therefore, EMA and EMI may be applied jointly and synergistically in future practice. For example, this approach could be extended to KOA populations by using real-time monitoring data to automatically deliver tailored coping strategies or emotional regulation guidance during periods of pain exacerbation or emotional distress.⁶⁰

Recent studies have applied AI-driven machine learning models to analyze time-series data generated from EMA, thereby developing individualized risk prediction models.^61,62 By integrating patients’ historical symptom trajectories, daily activity patterns, and environmental or contextual factors, these models enable early identification of high-risk states and promote a shift in disease management from passive assessment toward proactive prevention. In the future, such risk prediction frameworks could be integrated into EMA systems for individuals with KOA to identify predictors of multiple KOA-related symptoms and generate symptom-specific risk profiles,⁶³ thereby informing personalized exercise prescriptions and targeted interventions and substantially improving the timeliness and precision of clinical care.⁶⁴ Such optimization not only enhances nursing efficiency but also reduces health-care costs, with important implications for health economics.

4.1.3. Integration into clinical care

This study revealed that, with advances in science and technology, methods for collecting EMA data have gradually shifted from paper-based formats to mobile devices such as PDAs, wearable devices, and mobile applications. However, the KOA population is generally older and may demonstrate slower acceptance of emerging technologies. Difficulty in operating devices among older adults represents an important factor contributing to reduced motivation for participation.⁶⁵ Therefore, considering the discomfort experienced by elderly patients with KOA and the limitations of current data-collection technologies, future EMA applications should optimize user interfaces, improve system stability, reduce operational complexity, and enhance device wear comfort. With the rapid development of artificial intelligence, EMA systems are increasingly being integrated with intelligent technologies.⁶⁶ Recent studies have used the imaging, audio, and multifunctional capabilities of smart devices to support real-time monitoring of individual behaviors. In the future, passive data collection through wearable devices and sensor-based technologies, combined with EMA self-reports, may substantially reduce the reporting burden for patients with KOA, enable timely feedback on symptom patterns, and thereby improve adherence.^67,68

4.4. Limitations

This study has several limitations that should be considered when interpreting the findings. First, as a scoping review, this study was designed to map the available evidence and identify research gaps rather than to provide a graded assessment of evidence strength. Consistent with this objective, no formal methodological quality appraisal or risk-of-bias assessment was conducted, and no studies were excluded based on study quality. Therefore, the findings should be interpreted as a descriptive overview of EMA research characteristics and reporting practices, and caution is required when translating these findings into clinical or research recommendations. Second, the included studies demonstrated substantial heterogeneity in study design, methodology, and EMA protocol characteristics. Several studies did not report participant response, completion, or compliance rates, and response-related indicators were not reported consistently across studies. These issues may introduce uncertainty in interpreting participant engagement and limit cross-study comparability, particularly regarding feasibility and symptom-monitoring outcomes. Finally, most included studies were conducted in developed countries in Europe and North America, with limited evidence originating from Asia and other regions. Consequently, the findings may not fully represent broader KOA populations due to geographical, cultural, and technology-related differences. Previous research⁶⁸ has suggested that when EMA is used to evaluate and monitor relevant symptoms, a sample size of approximately 100 participants may be appropriate in certain contexts; however, sample sizes varied considerably across the included studies. Future research should determine appropriate sample sizes based on specific study objectives and EMA protocol characteristics, expand investigations in underrepresented regions, and adopt more transparent and standardized reporting of EMA protocol characteristics and response-related metrics.

5. Conclusions

In summary, this scoping review suggests that EMA is a feasible and clinically valuable approach for evaluating and dynamically monitoring symptoms in people with KOA. Across the included studies, EMA was mainly used to capture real-time symptom fluctuations and subjective experiences in daily life and to examine their associations with influencing factors. However, variability in EMA protocol design and data collection tools, as well as heterogeneity in the reporting of study findings across studies, may limit comparability and should be addressed in future research. Future studies should further optimize and validate EMA protocols and strengthen standardized reporting to support more robust evidence and the integration of EMA into personalized symptom management and digital care for patients with KOA.

Supplemental material

Supplemental material - Ecological momentary assessment for symptom monitoring among people with knee osteoarthritis: A scoping review

Supplemental material for Ecological momentary assessment for symptom monitoring among people with knee osteoarthritis: A scoping review by Peizhe Zhang, Chong Hou, Qingli Ren, Lingyu Liu, Xiujuan Guo, Yaqiong Chang, Jinli Guo in DIGITAL HEALTH.

Supplemental material

Supplemental material - Ecological momentary assessment for symptom monitoring among people with knee osteoarthritis: A scoping review

Supplemental material

Supplemental material - Ecological momentary assessment for symptom monitoring among people with knee osteoarthritis: A scoping review

Footnotes

Acknowledgment

The author thanked all. The authors have nothing to report.

ORCID iDs

Peizhe Zhang

Chong Hou

Qingli Ren

Lingyu Liu

Xiujuan Guo

Ethical considerations

There are no human participants in this article.

Consent to participate

Informed consent is not required.

Author contributions

PZZ was responsible for the conceptualization, data curation, formal analysis, investigation, methodology, project administration, original draft preparation, and review and editing. HC was responsible for the conceptualization, data curation, formal analysis, investigation, and review and editing. LYL, QLR, XJG, and YQC contributed equally to this work and were responsible for data curation, formal analysis, and review and editing. JLG was responsible for providing expertise, making suggestions, and critically revising the first draft.

Data Availability Statement

Data sharing not applicable to this article as no datasets were generated or analysed during the current study.*

Supplemental material

Supplemental material for this article is available online.

References

Steinmetz

Culbreth

Haile

. Global, regional, and national burden of osteoarthritis, 1990-2020 and projections to 2050: a systematic analysis for the Global Burden of Disease Study 2021. LANCET RHEUMATOLOGY 2023; 5: E508–E522. https://doi.org/10.1016/S2665-9913(23)00163-7

Yang

Wang

Liu

, et al. Burden of Knee Osteoarthritis in 204 Countries and Territories, 1990-2019: Results From the Global Burden of Disease Study 2019. Arthritis care & research 2023; 75: 2489–2500. https://doi.org/10.1002/acr.25158

Wang

Zhou

, et al. Bidirectional association between knee osteoarthritis and depressive symptoms: evidence from a nationwide population-based cohort. BMC musculoskeletal disorders 2022; 23: 213. https://doi.org/10.1186/s12891-022-05137-8

Duong

Ding

, et al. Evaluation and Treatment of Knee Pain: A Review. Jama 2023; 330: 1568–1580. https://doi.org/10.1001/jama.2023.19675

Aydemir

Huang

Foucher

. Strength and physical activity in osteoarthritis: The mediating role of kinesiophobia. Journal of orthopaedic research: official publication of the Orthopaedic Research Society 2022; 40: 1135–1142. 2021/07/30. https://doi.org/10.1002/jor.25151

Fukutani

Iijima

Aoyama

, et al. Knee pain during activities of daily living and its relationship with physical activity in patients with early and severe knee osteoarthritis. Clin Rheumatol 2016; 35: 2307–2316. https://doi.org/10.1007/s10067-016-3251-8

Agaliotis

Mackey

Jan

, et al. Burden of reduced work productivity among people with chronic knee pain: a systematic review. Occup Environ Med 2014; 71: 651–659. https://doi.org/10.1136/oemed-2013-101997

Mota

Tarricone

Ciani

, et al. Determinants of demand for total hip and knee arthroplasty: a systematic literature review. BMC Health Serv Res 2012; 12: 225. https://doi.org/10.1186/1472-6963-12-225

Wang

Huang

, et al. Factors influencing the decision to receive total knee replacement among patients with severe knee osteoarthritis under universal health insurance coverage. Sci Rep 2024; 14: 30028. https://doi.org/10.1038/s41598-024-81852-7

10.

Faye

Legrand

Le Gall

, et al. Five-Year Symptom Trajectories in Nondialysis-Dependent CKD Patients. Clinical journal of the American Society of Nephrology: CJASN 2022; 17: 1588–1597. https://doi.org/10.2215/cjn.06140522

11.

Collins

Yang

Opare-Addo

, et al. Quantifying sustained pain worsening in knee osteoarthritis. Osteoarthritis and cartilage 2023; 31: 802–808. https://doi.org/10.1016/j.joca.2023.03.012

12.

Chang

Almagor

Lee

, et al. The Natural History of Knee Osteoarthritis Pain Experience and Risk Profiles. The journal of pain 2023; 24: 2175–2185. https://doi.org/10.1016/j.jpain.2023.07.002

13.

Stone

Schwartz

Neale

, et al. A comparison of coping assessed by ecological momentary assessment and retrospective recall. Journal of personality and social psychology 1998; 74: 1670–1680. https://doi.org/10.1037//0022-3514.74.6.1670

14.

Burrows

Barry

Sturnieks

, et al. The Relationship Between Daily Physical Activity and Pain in Individuals with Knee Osteoarthritis. Pain medicine (Malden, Mass) 2020; 21: 2481–2495. https://doi.org/10.1093/pm/pnaa096

15.

Stone

Shiffman

. Ecological momentary assessment (EMA) in behavorial medicine. Annals of Behavioral Medicine 1994; 16: 199–202. https://doi.org/10.1093/abm/16.3.199

16.

Shiffman

Stone

Hufford

. Ecological momentary assessment. Annu Rev Clin Psychol 2008; 4: 1–32. https://doi.org/10.1146/annurev.clinpsy.3.022806.091415

17.

Stinson

Liu

Dallery

. Ecological Momentary Assessment: A Systematic Review of Validity Research. Perspectives on behavior science 2022; 45: 469–493. https://doi.org/10.1007/s40614-022-00339-w

18.

Murphy

Lyden

Kratz

, et al. Characterizing Pain Flares From the Perspective of Individuals With Symptomatic Knee Osteoarthritis. Arthritis care & research 2015; 67: 1103–1111. https://doi.org/10.1002/acr.22545

19.

Overton

Swain

Falling

, et al. Experiences and Perceptions of Using Smartphone Ecological Momentary Assessment for Reporting Knee Osteoarthritis Pain and Symptoms. The Clinical journal of pain 2023; 39: 442–451. https://doi.org/10.1097/ajp.0000000000001138

20.

Smith

DeCaro

Murphy

, et al. Momentary Reports of Fatigue Predict Physical Activity Level: Wrist, Waist, and Combined Accelerometry. Journal of aging and health 2020; 32: 921–925. 2019/07/20. https://doi.org/10.1177/0898264319863609

21.

Heijman

MWJ

van den Ende

CHM

Peters

YAS

, et al. Day-to-day fluctuations of fatigue severity in individuals with knee osteoarthritis: an ecological momentary assessment approach. Rheumatology advances in practice 2023; 7: rkad056. https://doi.org/10.1093/rap/rkad056

22.

Arksey

O'Malley

. Scoping studies: towards a methodological framework. International Journal of Social Research Methodology 2005; 8: 19–32. https://doi.org/10.1080/1364557032000119616

23.

Levac

Colquhoun

O’Brien

. Scoping studies: advancing the methodology. Implementation science: IS 2010; 5: 69. https://doi.org/10.1186/1748-5908-5-69

24.

Page

McKenzie

Bossuyt

, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. Bmj 2021; 372: n71. https://doi.org/10.1136/bmj.n71

25.

Khalil

Jia

Moraes

, et al. Scoping reviews and their role in identifying research priorities. Journal of clinical epidemiology 2025; 181: 111712. https://doi.org/10.1016/j.jclinepi.2025.111712

26.

Peters

MDJ

Marnie

Colquhoun

, et al. Scoping reviews: reinforcing and advancing the methodology and application. Systematic reviews 2021; 10: 263. https://doi.org/10.1186/s13643-021-01821-3

27.

Trudeau

Van Inwegen

Eaton

, et al. Assessment of pain and activity using an electronic pain diary and actigraphy device in a randomized, placebo-controlled crossover trial of celecoxib in osteoarthritis of the knee. Pain practice 2015; 15: 247–255, Journal article. https://doi.org/10.1111/papr.12167

28.

Murphy

Kratz

Williams

, et al. The Association between Symptoms, Pain Coping Strategies, and Physical Activity Among People with Symptomatic Knee and Hip Osteoarthritis. Frontiers in psychology 2012; 3: 326. https://doi.org/10.3389/fpsyg.2012.00326

29.

Focht

Gauvin

Rejeski

. The contribution of daily experiences and acute exercise to fluctuations in daily feeling states among older, obese adults with knee osteoarthritis. Journal of behavioral medicine 2004; 27: 101–121. https://doi.org/10.1023/b:jobm.0000019847.80315.4d

30.

Zhaoyang

Martire

Darnall

. Daily pain catastrophizing predicts less physical activity and more sedentary behavior in older adults with osteoarthritis. Pain 2020; 161: 2603–2610. https://doi.org/10.1097/j.pain.0000000000001959

31.

Martire

Stephens

MAP

Mogle

, et al. Daily Spousal Influence on Physical Activity in Knee Osteoarthritis. Annals of Behavioral Medicine 2013; 45: 213–223. https://doi.org/10.1007/s12160-012-9442-x

32.

Murphy

Smith

. Ecological measurement of fatigue and fatigability in older adults with osteoarthritis. The journals of gerontology Series A, Biological sciences and medical sciences 2010; 65: 184–189. 2009/09/25. https://doi.org/10.1093/gerona/glp137

33.

Rivera

Parmelee

Smith

. The impact of social interactions and pain on daily positive and negative affect in adults with osteoarthritis of the knee. Aging Ment Health 2020; 24: 8–14. 2018/11/02. https://doi.org/10.1080/13607863.2018.1506744

34.

Parmelee

Behrens

Costlow Hill

, et al. Momentary Associations of Osteoarthritis Pain and Affect: Depression as Moderator. The journals of gerontology Series B, Psychological sciences and social sciences 2022; 77: 1240–1249. 2021/12/06. https://doi.org/10.1093/geronb/gbab221

35.

Behrens

. Momentary Pain-Affect Associations and Moderating Stressful Life Events as Predictors of Global Depression in Osteoarthritis. PhD. The University of Alabama, 2023.

36.

Zhaoyang

Martire

Sliwinski

. Morning self-efficacy predicts physical activity throughout the day in knee osteoarthritis. Health psychology: official journal of the Division of Health Psychology, American Psychological Association 2017; 36: 568–576. https://doi.org/10.1037/hea0000479

37.

Choi

Somers

Babyak

, et al. The relationship between pain and eating among overweight and obese individuals with osteoarthritis: an ecological momentary study. Pain research & management 2014; 19: e159–e163. https://doi.org/10.1155/2014/598382

38.

Rouzaud

Cenko

Mardini

, et al. Satisfaction, Usability, and Compliance With the Use of Smartwatches for Ecological Momentary Assessment of Knee Osteoarthritis Symptoms in Older Adults: Usability Study. JMIR Aging 2021; 4: e24553. https://doi.org/10.2196/24553

39.

Mardini

Nerella

Kheirkhahan

, et al. The Temporal Relationship Between Ecological Pain and Life-Space Mobility in Older Adults With Knee Osteoarthritis: A Smartwatch-Based Demonstration Study. JMIR Mhealth Uhealth 2021; 9: e19609. https://doi.org/10.2196/19609

40.

Whibley

Braley

Kratz

, et al. Transient Effects of Sleep on Next-Day Pain and Fatigue in Older Adults With Symptomatic Osteoarthritis. The journal of pain 2019; 20: 1373–1382. https://doi.org/10.1016/j.jpain.2019.04.011

41.

Focht

Ewing

Gauvin

, et al. The unique and transient impact of acute exercise on pain perception in older, overweight, or obese adults with knee osteoarthritis. Annals of behavioral medicine: a publication of the Society of Behavioral Medicine 2002; 24: 201–210. https://doi.org/10.1207/s15324796abm2403_05

42.

Smith

Parmelee

. Within-Day Variability of Fatigue and Pain Among African Americans and Non-Hispanic Whites With Osteoarthritis of the Knee. Arthritis care & research 2016; 68: 115–122. 2015/09/01. https://doi.org/10.1002/acr.22690

43.

Overton

Swain

Falling

, et al. Activity-related pain and sensitization predict within- and between-person pain experience in people with knee osteoarthritis: An ecological momentary assessment study. Osteoarthritis and cartilage open 2024; 6: 100439. https://doi.org/10.1016/j.ocarto.2024.100439

44.

Overton

Swain

Falling

, et al. Understanding the biopsychosocial knee osteoarthritis pain experience: an ecological momentary assessment. Pain reports 2024; 9: e1172. https://doi.org/10.1097/pr9.0000000000001172

45.

Fawole

Dell’Isola

Steultjens

, et al. Temporal associations between physical activity, mental activity and fatigue dimensions in knee osteoarthritis: an exploratory intensive longitudinal study. Fatigue: Biomedicine, Health and Behavior 2020; 8: 32–48, Article. https://doi.org/10.1080/21641846.2020.1744807

46.

Chang

Hertel

Bruun

, et al. Temporal Associations of Physical Activity With Subsequent Knee Pain in Individuals With Knee Osteoarthritis: An Ecological Momentary Assessment Study. European journal of pain (London, England) 2025; 29: e70026. https://doi.org/10.1002/ejp.70026

47.

Garcia-Palacios

Herrero

Belmonte

, et al. Ecological momentary assessment for chronic pain in fibromyalgia using a smartphone: a randomized crossover study. European journal of pain (London, England) 2014; 18: 862–872. https://doi.org/10.1002/j.1532-2149.2013.00425.x

48.

, et al. On Patient Quality of Life: Impacts of Knee Osteoarthritis on Pain, Anxiety, Depression, Fatigue and Sleep Disorders. Nurs Open 2025; 12: e70264. https://doi.org/10.1002/nop2.70264

49.

Cox

Knowles

Jessup

, et al. Psychometric properties of an ecological momentary assessment measure of posttraumatic stress disorder (PTSD) symptoms. Psychological trauma: theory, research, practice and policy 2025, 2025. https://doi.org/10.1037/tra0001939

50.

Mengelkoch

Moriarity

Novak

, et al. Using Ecological Momentary Assessments to Study How Daily Fluctuations in Psychological States Impact Stress, Well-Being, and Health. J Clin Med 2023; 13: 2024–01/11. https://doi.org/10.3390/jcm13010024

51.

Ramsey

Wetherell

Depp

, et al. Feasibility and Acceptability of Smartphone Assessment in Older Adults with Cognitive and Emotional Difficulties. J Technol Hum Serv 2016; 34: 209–223. https://doi.org/10.1080/15228835.2016.1170649

52.

Ottenstein

Werner

. Compliance in Ambulatory Assessment Studies: Investigating Study and Sample Characteristics as Predictors. Assessment 2022; 29: 1765–1776. 2021/07/21. https://doi.org/10.1177/10731911211032718

53.

Wrzus

Neubauer

. Ecological Momentary Assessment: A Meta-Analysis on Designs, Samples, and Compliance Across Research Fields. Assessment 2023; 30: 825–846. 2022/01/13. https://doi.org/10.1177/10731911211067538

54.

May

Junghaenel

Ono

, et al. Ecological Momentary Assessment Methodology in Chronic Pain Research: A Systematic Review. The journal of pain 2018; 19: 699–716. https://doi.org/10.1016/j.jpain.2018.01.006

55.

Smyth

Jones

Wen

CKF

, et al. Influence of ecological momentary assessment study design features on reported willingness to participate and perceptions of potential research studies: an experimental study. BMJ Open 2021; 11: e049154. https://doi.org/10.1136/bmjopen-2021-049154

56.

Chen

Greene

, et al. A systematic review of ecological momentary assessment in autism research. Autism: the international journal of research and practice 2025; 29: 1374–1389. 2024/12/19. https://doi.org/10.1177/13623613241305722

57.

Hiller

Götzl

Rauschenberg

, et al. Health-Promoting Effects and Everyday Experiences With a Mental Health App Using Ecological Momentary Assessments and AI-Based Ecological Momentary Interventions Among Young People: Qualitative Interview and Focus Group Study. JMIR Mhealth Uhealth 2025; 13: e65106. https://doi.org/10.2196/65106

58.

Smith

Juarascio

. From Ecological Momentary Assessment (EMA) to Ecological Momentary Intervention (EMI): Past and Future Directions for Ambulatory Assessment and Interventions in Eating Disorders. Curr Psychiatry Rep 2019; 21: 53. https://doi.org/10.1007/s11920-019-1046-8

59.

Heron

Smyth

. Ecological momentary interventions: incorporating mobile technology into psychosocial and health behaviour treatments. Br J Health Psychol 2010; 15: 1–39. 2009/08/04. https://doi.org/10.1348/135910709x466063

60.

Ezekiel

Wilding

Dearling

, et al. Exploring the usefulness of real-time digitally supported fatigue monitoring in fatigue management: Perspectives from occupational therapists and brain injury survivors. The British journal of occupational therapy 2024; 87: 783–792. 2025/05/08. https://doi.org/10.1177/03080226241269247

61.

Bozzay

Hughes

Eickhoff

, et al. Identifying momentary suicidal ideation using machine learning in patients at high-risk for suicide. J Affect Disord 2024; 364: 57–64. https://doi.org/10.1016/j.jad.2024.08.038

62.

De la Barrera

Arrigoni

Monserrat

, et al. Using ecological momentary assessment and machine learning techniques to predict depressive symptoms in emerging adults. Psychiatry research 2024; 332: 115710. https://doi.org/10.1016/j.psychres.2023.115710

63.

Leroux

Crainiceanu

Zeger

, et al. Statistical modeling of acute and chronic pain patient-reported outcomes obtained from ecological momentary assessment. Pain 2024; 165: 1955–1965. https://doi.org/10.1097/j.pain.0000000000003214

64.

Andrews

Ireland

Deen

, et al. Clinical utility of a mHealth assisted intervention for activity modulation in chronic pain: The pilot implementation of pain ROADMAP. European journal of pain (London, England) 2023; 27: 749–765. https://doi.org/10.1002/ejp.2104

65.

Kokorelias

Grigorovich

Harris

, et al. Longitudinal Coadaptation of Older Adults With Wearables and Voice-Activated Virtual Assistants: Scoping Review. J Med Internet Res 2024; 26: e57258. https://doi.org/10.2196/57258

66.

Schembre

Liao

O'Connor

, et al. Mobile Ecological Momentary Diet Assessment Methods for Behavioral Research: Systematic Review. JMIR Mhealth Uhealth 2018; 6: e11170. https://doi.org/10.2196/11170

67.

Frank

Pong

Asher

, et al.

Smart phone technologies and ecological momentary data: is this the way forward on depression management and research?

Curr Opin Psychiatry 2018; 31: 3–6. 2017/10/31. https://doi.org/10.1097/yco.0000000000000382

68.

Yao

Yang

Wang

, et al. Compliance with ecological momentary assessment programmes in the elderly: a systematic review and meta-analysis. BMJ Open 2023; 13: e069523. https://doi.org/10.1136/bmjopen-2022-069523