Engagement and Participant Experiences With Consumer Smartwatches for Health Research: Longitudinal, Observational Feasibility Study

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.

Design and Feasibility Analysis of a Smartphone-Based Digital Cognitive Assessment Study in the Framingham Heart Study

BACKGROUND

Smartphone-based digital technology is increasingly being recognized as a cost-effective, scalable, and noninvasive method of collecting longitudinal cognitive and behavioral data. Accordingly, a state-of-the-art 3-year longitudinal project focused on collecting multimodal digital data for early detection of cognitive impairment was developed.

METHODS AND RESULTS

A smartphone application collected 2 modalities of cognitive data, digital voice and screen-based behaviors, from the FHS (Framingham Heart Study) multigenerational Generation 2 (Gen 2) and Generation 3 (Gen 3) cohorts. To understand the feasibility of conducting a smartphone-based study, participants completed a series of questions about their smartphone and app use, as well as sensory and environmental factors that they encountered while completing the tasks on the app. Baseline data collected to date were from 537 participants (mean age=66.6 years, SD=7.0; 58.47% female). Across the younger participants from the Gen 3 cohort (n=455; mean age=60.8 years, SD=8.2; 59.12% female) and older participants from the Gen 2 cohort (n=82; mean age=74.2 years, SD=5.8; 54.88% female), an average of 76% participants agreed or strongly agreed that they felt confident about using the app, 77% on average agreed or strongly agreed that they were able to use the app on their own, and 81% on average rated the app as easy to use.

CONCLUSIONS

Based on participant ratings, the study findings are promising. At baseline, the majority of participants are able to complete the app-related tasks, follow the instructions, and encounter minimal barriers to completing the tasks independently. These data provide evidence that designing and collecting smartphone application data in an unsupervised, remote, and naturalistic setting in a large, community-based population is feasible.

Participant Engagement and Adherence to Providing Smartwatch and Patient-Reported Outcome Data: Digital Tracking of Rheumatoid Arthritis Longitudinally (DIGITAL) Real-World Study

BACKGROUND

Digital health studies using electronic patient-reported outcomes (ePROs) and wearables bring new challenges, including the need for participants to consistently provide trial data.

OBJECTIVE

This study aims to characterize the engagement, protocol adherence, and data completeness among participants with rheumatoid arthritis enrolled in the Digital Tracking of Arthritis Longitudinally (DIGITAL) study.

METHODS

Participants were invited to participate in this app-based study, which included a 14-day run-in and an 84-day main study. In the run-in period, data were collected via the ArthritisPower mobile app to increase app familiarity and identify the individuals who were motivated to participate. Successful completers of the run-in period were mailed a wearable smartwatch, and automated and manual prompts were sent to participants, reminding them to complete app input or regularly wear and synchronize devices, respectively, during the main study. Study coordinators monitored participant data and contacted participants via email, SMS text messaging, and phone to resolve adherence issues per a priori rules, in which consecutive spans of missing data triggered participant contact. Adherence to data collection during the main study period was defined as providing requested data for >70% of 84 days (daily ePRO, ≥80% daily smartwatch data) or at least 9 of 12 weeks (weekly ePRO).

RESULTS

Of the 470 participants expressing initial interest, 278 (59.1%) completed the run-in period and qualified for the main study. Over the 12-week main study period, 87.4% (243/278) of participants met the definition of adherence to protocol-specified data collection for weekly ePRO, and 57.2% (159/278) did so for daily ePRO. For smartwatch data, 81.7% (227/278) of the participants adhered to the protocol-specified data collection. In total, 52.9% (147/278) of the participants met composite adherence.

CONCLUSIONS

Compared with other digital health rheumatoid arthritis studies, a short run-in period appears useful for identifying participants likely to engage in a study that collects data via a mobile app and wearables and gives participants time to acclimate to study requirements. Automated or manual prompts (ie, "It's time to sync your smartwatch") may be necessary to optimize adherence. Adherence varies by data collection type (eg, ePRO vs smartwatch data).

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.2196/14665.

Patient Perceptions of Wearable and Smartphone Technologies for Remote Outcome Monitoring in Total Knee Arthroplasties

While there is enthusiasm for wearables and smartphone technologies in evaluating clinical outcomes among clinicians, less is known about the willingness of patients who have osteoarthritis (OA) to consent for remote outcome monitoring. We developed an Institutional Review Board-approved questionnaire to assess patient perceptions of remote monitoring technologies in a high-volume orthopaedic clinical center. Fifty total knee arthroplasty (TKA) patients (56% female; mean age: 61 years, range: 23-89) and fifty nonoperative OA knee patients (54% female; mean age: 58 years, range: 25-89) routinely consulted in the clinic as part of their OA treatment and consented to participate in the study. Patient perceptions were compared using Pearson's chi-square analyses with a significance threshold of p < 0.05. We found that TKA patients were more receptive to the use of smartphone apps (84 vs. 60%, p = 0.008) and wearable sensors (80 vs. 48%, p < 0.001) and learning to use custom wearables (72 vs. 38%, p = 0.002) than nonoperative OA knee patients as part of their treatment. Likewise, the majority of TKA patients were willing to use the global positioning system in their postoperative technology (54 vs. 18%, p < 0.001), especially if they were only active during certain circumstances (62 vs. 24%, p < 0.001). TKA patients also expressed willingness to have their body movement (68%), balance (70%), sleep (76%), and cardiac output (80%) tracked using remote technologies. Overall, we found that TKA patients were highly receptive to using wearable technology in their treatments, whereas nonoperative OA knee patients were generally unreceptive. Our study challenges the concept that current wearable technology approaches will be generally effective as a tool to remotely monitor all patients across the OA severity landscape.

Impact of VA's video telehealth tablets on substance use disorder care during the COVID-19 pandemic

BACKGROUND

Telehealth has the potential to improve health care access for patients but it has been underused and understudied for examining patients with substance use disorders (SUD). VA began distributing video-enabled tablets to veterans with access barriers in 2016 to facilitate participation in home-based telehealth and expanded this program in 2020 due to the coronavirus COVID-19 pandemic.

OBJECTIVE

Examine the impact of VA's video-enabled telehealth tablets on mental health services for patients diagnosed with SUD.

METHODS

This study included VA patients who had ≥1 mental health visit in the calendar year 2019 and a documented diagnosis of SUD. Using difference-in-differences and event study designs, we compared outcomes for SUD-diagnosed patients who received a video-enabled tablet from VA between March 15th, 2020 and December 31st, 2021 and SUD-diagnosed patients who never received VA tablets, 10 months before and after tablet-issuance. Outcomes included monthly frequency of SUD psychotherapy visits, SUD specialty group therapy visits and SUD specialty individual outpatient visits. We examined changes in video visits and changes in visits across all modalities of care (video, phone, and in-person). Regression models adjusted for several covariates such as age, sex, rurality, race, ethnicity, physical and mental health chronic conditions, and broadband coverage in patients' residential zip-code.

RESULTS

The cohort included 21,684 SUD-diagnosed tablet-recipients and 267,873 SUD-diagnosed non-recipients. VA's video-enabled tablets were associated with increases in video visits for SUD psychotherapy (+3.5 visits/year), SUD group therapy (+2.1 visits/year) and SUD individual outpatient visits (+1 visit/year), translating to increases in visits across all modalities (in-person, phone and video): increase of 18 % for SUD psychotherapy (+1.9 visits/year), 10 % for SUD specialty group therapy (+0.5 visit/year), and 4 % for SUD specialty individual outpatient treatment (+0.5 visit/year).

CONCLUSIONS

VA's distribution of video-enabled tablets during the COVID-19 pandemic were associated with higher engagement with video-based services for SUD care among patients diagnosed with SUD, translating to modest increases in total visits across in-person, phone and video modalities. Distribution of video-enabled devices can offer patients critical continuity of SUD therapy, particularly in scenarios where they have heightened barriers to in-person care.

Day-to-day variability of knee pain and the relationship with physical activity in people with knee osteoarthritis: an observational, feasibility study using consumer smartwatches

OBJECTIVE

To assess the feasibility of using smartwatches in people with knee osteoarthritis (OA) to determine the day-to-day variability of pain and the relationship between daily pain and step count.

DESIGN

Observational, feasibility study.

SETTING

In July 2017, the study was advertised in newspapers, magazines and, on social media. Participants had to be living/willing to travel to Manchester. Recruitment was in September 2017 and data collection was completed in January 2018.

PARTICIPANTS

26 participants aged>50 years with self-diagnosed symptomatic knee OA were recruited.

OUTCOME MEASURES

Participants were provided with a consumer cellular smartwatch with a bespoke app that triggered a series of daily questions including two times per day questions about level of knee pain and one time per month question from the pain subscale of the Knee Injury and Osteoarthritis Outcome Score (KOOS) questionnaire. The smartwatch also recorded daily step counts.

RESULTS

Of the 25 participants, 13 were men and their mean age was 65 years (standard deviation (SD) 8 years). The smartwatch app was successful in simultaneously assessing and recording data on knee pain and step count in real time. Knee pain was categorised into sustained high/low or fluctuating levels, but there was considerable day-to-day variation within these categories. Levels of knee pain in general correlated with pain assessed by KOOS. Those with sustained high/low levels of pain had a similar daily step count average (mean 3754 (SD 2524)/4307 (SD 2992)), but those with fluctuating pain had much lower step count levels (mean 2064 (SD 1716)).

CONCLUSIONS

Smartwatches can be used to assess pain and physical activity in knee OA. Larger studies may help inform a better understanding of causal links between physical activity patterns and pain. In time, this could inform development of personalised physical activity recommendations for people with knee OA.

The use of technology to support lifestyle interventions in knee osteoarthritis: A scoping review

Introduction

Technological tools that promote the adoption of physical activity to increase individuals' functional ability in knee osteoarthritis (OA) are desired to support lifestyle interventions. However, there is little consensus as to the current use of such supportive interventions for knee OA. The aim of this scoping review is therefore to provide an overview on the current use of technology within lifestyle interventions for individuals with knee OA.

Methods

Scoping review as per PRISMA guidance. Structured search of Cochrane Central Register for Controlled Trials, ELSEVIER, IEEExplore, GOOGLE Scholar, MEDLINE, PEDRO, PUBMED, WEB OF SCIENCE from 2010 to 2020 inclusive. Hits were screened by title and abstract and then full text review based on pre-defined criteria. Results were synthesised and pooled by theme for reporting.

Results

2508 papers were identified, and following review, 78 studies included. Papers included interventions for individuals with knee osteoarthritis (n ​= ​31), total or partial knee arthroplasty (n ​= ​20) and developmental work in healthy controls (n ​= ​27). Of the 78 studies, 47 were carried out in laboratory settings and 31 in the field. The identified themes included Movement measurement (n ​= ​24), Tele-rehabilitation (n ​= ​22), Biofeedback (n ​= ​20), Directly applied interventions (n ​= ​3), Virtual or augmented reality (n ​= ​5) and Machine learning (n ​= ​4).

Conclusions

The predominant current use of technology in OA lifestyle interventions is through well-established telecommunication and commercially available activity, joint angle and loading based measurement devices, while integrating new advanced technologies seems a longer-term goal. There is great potential for the engineering and clinical community to use technology to develop systems that offer real-time feedback to patients and clinician as part of rehabilitative interventions to inform treatment.

Self-Management of Subclinical Common Mental Health Disorders (Anxiety, Depression and Sleep Disorders) Using Wearable Devices

RATIONALE

Common mental health disorders (CMD) (anxiety, depression, and sleep disorders) are among the leading causes of disease burden globally. The economic burden associated with such disorders is estimated at $2.4 trillion as of 2010 and is expected to reach $16 trillion by 2030. The UK has observed a 21-fold increase in the economic burden associated with CMD over the past decade. The recent COVID-19 pandemic was a catalyst for adopting technologies for mental health support and services, thereby increasing the reception of personal health data and wearables. Wearables hold considerable promise to empower users concerning the management of subclinical common mental health disorders. However, there are significant challenges to adopting wearables as a tool for the self-management of the symptoms of common mental health disorders.

AIMS

This review aims to evaluate the potential utility of wearables for the self-management of sub-clinical anxiety and depressive mental health disorders. Furthermore, we seek to understand the potential of wearables to reduce the burden on the healthcare system.

METHODOLOGY

a systematic review of research papers was conducted, focusing on wearable devices for the self-management of CMD released between 2018-2022, focusing primarily on mental health management using technology.

RESULTS

We screened 445 papers and analysed the reports from 12 wearable devices concerning their device type, year, biometrics used, and machine learning algorithm deployed. Electrodermal activity (EDA/GSR/SC/Skin Temperature), physical activity, and heart rate (HR) are the most common biometrics with nine, six and six reference counts, respectively. Additionally, while smartwatches have greater penetration and integration within the marketplace, fitness trackers have the most significant public value benefit of £513.9 M, likely due to greater retention.

Usability of a continuous oxygen saturation device for home telemonitoring

Background

The emergency for the COVID-19 pandemic has led to greater use of home telemonitoring devices. The aim of this study was to assess the usability of continuous home-monitoring care with an oxygen saturation device on post-COVID-19 patients.

Method

The system consists of a digital continuous pulse oximeter and a smartphone with an App, which were provided to patients. A survey composed of a standard Post-Study System Usability Questionnaire, and a satisfaction questionnaire was exploited to conduct a usability and feasibility analysis of the service.

Results

A total of 29 patients (17.2% female) with a mean age of 65 ± 11.5 years were enrolled: 20 patients were smartphone users (69%) with a mean age of 60.2 ± 9.5 years, and 9 patients (31%) did not own a smartphone (mean age 76.8 ± 5.9). The monitoring period was 1 month: a total of 444 recordings were conducted, 15 recordings per patient averagely. In total, 82% of the recordings performed did not require any intervention, while 18% led to the production of a report and subsequent intervention by a nurse who verified, together with the specialist, the need to intervene (i.e. the patient accessed the clinic for medical control and/or modification of oxygen therapy). A total of 17 patients compiled a usability questionnaire. The service was perceived as useful and well-structured, although it often required caregiver support.

Conclusions

Using continuous home-monitoring care with an oxygen saturation device seems feasible and useful for patients who could be followed at home avoiding going back to the hospital every time a trend oximetry is needed. Further improvements in connections, data flow processes, and simplifications, based on patients' feedback, are needed to scale up the service.

Factors associated with long-term use of digital devices in the electronic Framingham Heart Study

Long-term use of digital devices is critical for successful clinical or research use, but digital health studies are challenged by a rapid drop-off in participation. A nested e-cohort (eFHS) is embedded in the Framingham Heart Study and uses three system components: a new smartphone app, a digital blood pressure (BP) cuff, and a smartwatch. This study aims to identify factors associated with the use of individual eFHS system components over 1-year. Among 1948 eFHS enrollees, we examine participants who returned surveys within 90 days (n = 1918), and those who chose to use the smartwatch (n = 1243) and BP cuff (n = 1115). For each component, we investigate the same set of candidate predictors for usage and use generalized linear mixed models to select predictors (P < 0.1, P value from Z test statistic), adjusting for age, sex, and time (app use: 3-month period, device use: weekly). A multivariable model with the predictors selected from initial testing is used to identify factors associated with use of components (P < 0.05, P value from Z test statistic) adjusting for age, sex, and time. In multivariable models, older age is associated with higher use of all system components. Female sex and higher education levels are associated with higher completion of app-based surveys whereas higher scores for depressive symptoms, and lower than excellent self-rated health are associated with lower use of the smartwatch over the 12-month follow-up. Our findings show that sociodemographic and health related factors are significantly associated with long-term use of digital devices. Future research is needed to test interventional strategies focusing on these factors to evaluate improvement in long-term engagement.

Engagement and attrition with eHealth tools for remote monitoring in chronic arthritis: a systematic review and meta-analysis

Objectives

Although eHealth tools are potentially useful for remote disease monitoring, barriers include concerns of low engagement and high attrition. We aimed to summarise evidence on patients’ engagement and attrition with eHealth tools for remotely monitoring disease activity/impact in chronic arthritis.

Methods

A systematic literature search was conducted for original articles and abstracts published before September 2022. Eligible studies reported quantitative measures of patients’ engagement with eHealth instruments used for remote monitoring in chronic arthritis. Engagement rates were pooled using random effects meta-analysis.

Results

Of 8246 references, 45 studies were included: 23 using smartphone applications, 13 evaluating wearable activity trackers, 7 using personal digital assistants, 6 including web-based platforms and 2 using short message service. Wearable-based studies mostly reported engagement as the proportion of days the tracker was worn (70% pooled across 6 studies). For other eHealth tools, engagement was mostly reported as completion rates for remote patient-reported outcomes (PROs). The pooled completion rate was 80%, although between-study heterogeneity was high (I² 93%) with significant differences between eHealth tools and frequency of PRO-collection. Engagement significantly decreased with longer study duration, but attrition varied across studies (0%–89%). Several predictors of higher engagement were reported. Data on the influence of PRO-reporting frequency were conflicting.

Conclusion

Generally high patient engagement was reported with eHealth tools for remote monitoring in chronic arthritis. However, we found considerable between-study heterogeneity and a relative lack of real-world data. Future studies should use standardised measures of engagement, preferably assessed in a daily practice setting.

Trial registeration number

The protocol was registered on PROSPERO (CRD42021267936).

Longitudinal comparison of the self-entry Amyotrophic Lateral Sclerosis Functional Rating Scale-Revised (ALSFRS-RSE) and Rasch-Built Overall Amyotrophic Lateral Sclerosis Disability Scale (ROADS) as outcome measures in people with amyotrophic lateral sclerosis

INTRODUCTION/AIMS

Improved functional outcome measures in amyotrophic lateral sclerosis (ALS) would aid ALS trial design and help hasten drug discovery. We evaluate the longitudinal performance of the Rasch-Built Overall Amyotrophic Lateral Sclerosis Disability Scale (ROADS) compared to the Amyotrophic Lateral Sclerosis Functional Rating Scale Revised for Self-Entry (ALSFRS-RSE) as patient reported outcomes of functional status in people with ALS.

METHODS

Participants completed the ROADS and the ALSFRS-RSE questionnaires at baseline, 3-, 6-, and 12- mo using Research Electronic Data Capture as part of a prospective, longitudinal, remote, online survey study of fatigue in ALS from 9/2020 to 12/2021. The scales were compared cross-sectionally (at baseline) and longitudinally. Correlation coefficients, coefficients of variation, and descriptive statistics were assessed.

RESULTS

A total of 182 adults with ALS consented to the study. This volunteer sample was comprised of predominantly White, non-Hispanic, non-smoking participants. Consented participant survey completion was approximately 90% at baseline and greater than 40% at 12 mo. The ALSFRS-RSE and the ROADS had high, significant agreement at 3 and 6 mo by Cohen's kappa ≥71% (p < 0.001); the number of functional increases or plateaus on the two scales were not significantly different; and the coefficient of variation of functional decline was similar at the 6-month mark, though higher for the ROADS at 3 mo and lower at 12 mo.

DISCUSSION

Although the ROADS performed similarly to the ALSFRS-RSE in an observational cohort, it has psychometric advantages, such as Rasch-modeling and unidimensionality. It merits further investigation as a patient reported outcome of overall disability and efficacy outcome measure in ALS trials.

Patient Perceptions of Wearable and Smartphone Technologies for Remote Outcome Monitoring in Patients Who Have Hip Osteoarthritis or Arthroplasties

BACKGROUND

Although there is interest in wearables and smartphone technologies for remote outcome monitoring, little is known regarding the willingness of hip osteoarthritis (OA) and/or total hip arthroplasty (THA) patients to authorize and adhere to such treatment.

METHODS

We developed an Institutional Review Board-approved questionnaire to evaluate patient perceptions of remote monitoring technologies in a high-volume orthopedic center. Forty-seven THA patients (60% female; mean age: 66 years) and 50 nonoperative OA hip patients (52% female; mean age: 63 years) participated. Patient perceptions were compared using Pearson's chi-squared analyses.

RESULTS

THA patients were similarly interested in the use of smartphone apps (91% vs 94%, P = .695) in comparison to nonoperative hip OA patients. THA patients were more receptive to using wearable sensors (94% vs 44%, P < .001) relative to their nonoperative counterparts. THA patients also expressed stronger interest in learning to use custom wearables (87% vs 32%, P < .001) vs nonoperative patients. Likewise, the majority of THA patients were willing to use Global Positioning System technology (74% vs 26%, P < .001). THA patients also expressed willingness to have their body movement (89%), balance (89%), sleep (87%), and cardiac output (91%) tracked using remote technology.

CONCLUSION

Overall, we found that THA patients were highly receptive to using wearable technology in their treatments. Nonoperative OA hip patients were generally unreceptive to using smart technologies, with the exception of smartphone applications. This information may be useful as utilization of these technologies for patient care continues to evolve.

Assessing Pain Research: A Narrative Review of Emerging Pain Methods, Their Technosocial Implications, and Opportunities for Multidisciplinary Approaches

Pain research traverses many disciplines and methodologies. Yet, despite our understanding and field-wide acceptance of the multifactorial essence of pain as a sensory perception, emotional experience, and biopsychosocial condition, pain scientists and practitioners often remain siloed within their domain expertise and associated techniques. The context in which the field finds itself today-with increasing reliance on digital technologies, an on-going pandemic, and continued disparities in pain care-requires new collaborations and different approaches to measuring pain. Here, we review the state-of-the-art in human pain research, summarizing emerging practices and cutting-edge techniques across multiple methods and technologies. For each, we outline foreseeable technosocial considerations, reflecting on implications for standards of care, pain management, research, and societal impact. Through overviewing alternative data sources and varied ways of measuring pain and by reflecting on the concerns, limitations, and challenges facing the field, we hope to create critical dialogues, inspire more collaborations, and foster new ideas for future pain research methods.

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.

An Integrated, Multimodal, Digital Health Solution for Chronic Obstructive Pulmonary Disease: A Prospective Observational Pilot Study

Background

Chronic obstructive pulmonary disease (COPD) affects millions of Americans and has a high economic impact partially due to frequent emergency room visits and hospitalizations. Advances in digital health have made it possible to collect data remotely from multiple devices to assist in managing chronic diseases such as COPD.

Objective

In this pilot study, we evaluated the ability of patients with COPD to use the Wellinks mHealth platform to collect information from multiple modalities important to the management of COPD. We also assessed patient satisfaction and engagement with the platform.

Methods

A single-site, observational, prospective pilot study (N=19) was conducted using the Wellinks platform in adults with COPD. All patients were aged over 30 years at screening, owned an iPhone, and were currently undergoing a treatment regimen that included nebulized therapy. Enrolled patients received a study kit consisting of the Flyp nebulizer, Smart One spirometer, the Nonin pulse oximeter, plus the Wellinks mHealth app, and training for all devices. For 8 weeks, participants were to enter daily symptoms and medication use manually; spirometry, nebulizer, and pulse oximeter data were automatically recorded. Data were sent to the attending physician in a monthly report. Patient satisfaction was measured via a 5-point scale and the Net Promoter Score (NPS) captured in interviews at the end of the observation period.

Results

Average age of the patients was 79.6 (range 65-95) years. Participants (10 female; 9 male) had an average FEV₁% (forced expiratory volume in 1 second as % of predicted for the patient) of 56.2% of predicted (range 23%-113%) and FEV₁/forced vital capacity of 65%. COPD severity, as assessed by the Global Initiative for Chronic Obstructive Lung Disease (GOLD) classification, was mild in 2 patients, moderate in 6, and severe/very severe in 11; 9 patients were on home oxygen. During this 8-week study, average use of the spirometer was 2.5 times/week, and the pulse oximeter 4.2 times/week. Medication use was manually documented 9.0 times/week, nebulizer use 1.9 times/week, and symptoms recorded 1.2 times/week on average. The correlation coefficients of home to office measurements for peak flow and FEV₁ were high (r=0.94 and 0.96, respectively). Patients found the app valuable (13/16, 81%) and easy to use (15/16, 94%). The NPS was 59.

Conclusions

This study demonstrates that our cohort of patients with COPD engaged with the Wellinks mHealth platform avidly and consistently over the 8-week period, and that patient satisfaction was high, as indicated by the satisfaction survey and the NPS of 59. In this small, selected sample, patients were both willing to use the technology and capable of doing so successfully regardless of disease severity, age, or gender. The Wellinks mHealth platform was considered useful and valuable by patients, and can assist clinicians in improved, timely decision making for better COPD management.

Using Smartphones to Reduce Research Burden in a Neurodegenerative Population and Assessing Participant Adherence: A Randomized Clinical Trial and Two Observational Studies

Background

Smartphone studies provide an opportunity to collect frequent data at a low burden on participants. Therefore, smartphones may enable data collection from people with progressive neurodegenerative diseases such as amyotrophic lateral sclerosis at high frequencies for a long duration. However, the progressive decline in patients’ cognitive and functional abilities could also hamper the feasibility of collecting patient-reported outcomes, audio recordings, and location data in the long term.

Objective

The aim of this study is to investigate the completeness of survey data, audio recordings, and passively collected location data from 3 smartphone-based studies of people with amyotrophic lateral sclerosis.

Methods

We analyzed data completeness in three studies: 2 observational cohort studies (study 1: N=22; duration=12 weeks and study 2: N=49; duration=52 weeks) and 1 clinical trial (study 3: N=49; duration=20 weeks). In these studies, participants were asked to complete weekly surveys; weekly audio recordings; and in the background, the app collected sensor data, including location data. For each of the three studies and each of the three data streams, we estimated time-to-discontinuation using the Kaplan–Meier method. We identified predictors of app discontinuation using Cox proportional hazards regression analysis. We quantified data completeness for both early dropouts and participants who remained engaged for longer.

Results

Time-to-discontinuation was shortest in the year-long observational study and longest in the clinical trial. After 3 months in the study, most participants still completed surveys and audio recordings: 77% (17/22) in study 1, 59% (29/49) in study 2, and 96% (22/23) in study 3. After 3 months, passively collected location data were collected for 95% (21/22), 86% (42/49), and 100% (23/23) of the participants. The Cox regression did not provide evidence that demographic characteristics or disease severity at baseline were associated with attrition, although it was somewhat underpowered. The mean data completeness was the highest for passively collected location data. For most participants, data completeness declined over time; mean data completeness was typically lower in the month before participants dropped out. Moreover, data completeness was lower for people who dropped out in the first study month (very few data points) compared with participants who adhered long term (data completeness fluctuating around 75%).

Conclusions

These three studies successfully collected smartphone data longitudinally from a neurodegenerative population. Despite patients’ progressive physical and cognitive decline, time-to-discontinuation was higher than in typical smartphone studies. Our study provides an important benchmark for participant engagement in a neurodegenerative population. To increase data completeness, collecting passive data (such as location data) and identifying participants who are likely to adhere during the initial phase of a study can be useful.

Trial Registration

ClinicalTrials.gov NCT03168711; https://clinicaltrials.gov/ct2/show/NCT03168711

Engagement with consumer smartwatches for tracking symptoms of individuals living with multiple long-term conditions (multimorbidity): A longitudinal observational study

Introduction

People living with multiple long-term conditions (multimorbidity) (MLTC-M) experience an accumulating combination of different symptoms. It has been suggested that these symptoms can be tracked longitudinally using consumer technology, such as smartphones and wearable devices.

Aim

The aim of this study was to investigate longitudinal user engagement with a smartwatch application, collecting survey questions and active tasks over 90 days, in people living with MLTC-M.

Methods

‘Watch Your Steps’ was a prospective observational study, administering multiple questions and active tasks over 90 days. Adults with more than one clinician-diagnosed long-term conditions were loaned Fossil® Sport smartwatches, pre-loaded with the study app. Around 20 questions were prompted per day.

Daily completion rates were calculated to describe engagement patterns over time, and to explore how these varied by patient characteristics and question type.

Results

Fifty three people with MLTC-M took part in the study. Around half were male ( = 26; 49%) and the majority had a white ethnic background (n = 45; 85%). About a third of participants engaged with the smartwatch app nearly every day. The overall completion rate of symptom questions was 45% inter-quartile range (IQR 23–67%) across all study participants. Older patients and those with greater MLTC-M were more engaged, although engagement was not significantly different between genders.

Conclusion

It was feasible for people living with MLTC-M to report multiple symptoms per day over 3 months. User engagement appeared as good as other mobile health studies that recruited people with single health conditions, despite the higher daily data entry burden.

Responsiveness of an activity tracker as a measurement tool in a knee osteoarthritis clinical trial (ACTIVe-OA study)

BACKGROUND

In osteoarthritis (OA) clinical trials, reliable and responsive outcome measures to document physical and functional improvements are limited.

OBJECTIVE

This study aimed to assess whether the use of an activity tracker in an OA clinical trial is a responsive measurement tool. Secondary objectives assessed feasibility and validity.

METHODS

We recruited 65 participants in a prospective cohort study nested in a placebo-controlled clinical trial of platelet-rich plasma injection in knee OA. Participants wore an activity tracker (Fitbit Flex 2), and a smartphone was preloaded with a mobile application (OApp) designed to monitor load rates as a surrogate of knee loading. Participants used the systems for 7 days at baseline and for 7 days before the 2-month follow-up assessment. Effect size (ES) and standardised response mean (SRM) were calculated for change in step count and knee loading rate and regularly used knee OA outcome measures. Correlation coefficients (r) were calculated to examine the strength of the association between outcome measures.

RESULTS

Step count showed a trivial ES and SRM and mean knee loading rate measurements a moderate ES and SRM. We found a weak but significant correlation between change in mean steps per day and global improvement overall (r = 0.28) and Western Western Ontario and McMaster Universities Osteoarthritis Index function (r = -0.28). Compliance was high with the activity trackers.

CONCLUSIONS

Despite good feasibility, this study did not show significant responsiveness or validity of the activity trackers as compared with currently recommended outcome measures in OA clinical trials. The main challenge is the lack of a gold standard outcome measure to validate against, and because of the complex interplay between pain and measured function, a lack of correlation does not necessarily represent a failed validation in this context. Australian New Zealand Clinical Trials Registry: ACTRN12617000853347. This trial is a substudy of the "Platelet-rich plasma as a symptom-and disease-modifying treatment for knee osteoarthritis - the RESTORE trial".

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.

Assessing the Influence of Physical Activity Upon the Experience Sampling Response Rate on Wrist-Worn Devices

The Experience Sampling Method (ESM) is gaining ground for collecting self-reported data from human participants during daily routines. An important methodological challenge is to sustain sufficient response rates, especially when studies last longer than a few days. An obvious strategy is to deliver the experiential questions on a device that study participants can access easily at different times and contexts (e.g., a smartwatch). However, responses may still be hampered if the prompts are delivered at an inconvenient moment. Advances in context sensing create new opportunities for improving the timing of ESM prompts. Specifically, we explore how physiological sensing on commodity-level smartwatches can be utilized in triggering ESM prompts. We have created Experiencer, a novel ESM smartwatch platform that allows studying different prompting strategies. We ran a controlled experiment (N=71) on Experiencer to study the strengths and weaknesses of two sampling regimes. One group (N=34) received incoming notifications while resting (e.g., sedentary), and another group (N=37) received similar notifications while being active (e.g., running). We hypothesized that response rates would be higher when experiential questions are delivered during lower levels of physical activity. Contrary to our hypothesis, the response rates were found significantly higher in the active group, which demonstrates the relevance of studying dynamic forms of experience sampling that leverage better context-sensitive sampling regimes. Future research will seek to identify more refined strategies for context-sensitive ESM using smartwatches and further develop mechanisms that will enable researchers to easily adapt their prompting strategy to different contextual factors.

How Self-tracking and the Quantified Self Promote Health and Well-being: Systematic Review

BACKGROUND

Self-tracking technologies are widely used in people's daily lives and health care. Academic research on self-tracking and the quantified self has also accumulated rapidly in recent years. Surprisingly, there is a paucity of research that reviews, classifies, and synthesizes the state of the art with respect to self-tracking and the quantified self.

OBJECTIVE

Our objective was to identify the state of the art of self-tracking and the quantified self in terms of health and well-being.

METHODS

We have undertaken a systematic literature review on self-tracking and the quantified self in promoting health and well-being. After a rigorous literature search, followed by inclusions, exclusions, and the application of article quality assessment protocols, 67 empirical studies qualified for the review.

RESULTS

Our results demonstrate that prior research has focused on 3 stakeholders with respect to self-tracking and the quantified self, namely end users, patients and people with illnesses, and health care professionals and caregivers. We used these stakeholder groups to cluster the research themes of the reviewed studies. We identified 11 research themes. There are 6 themes under the end-user cluster: user motivation and goal setting, usage and effects of self-tracking, continuance intention and long-term usage, management of personal data, rejection and discontinuance, and user characteristics. The patient and people with illnesses cluster contains three themes: usage experience of patients and people with illnesses, management of patient-generated data, and advantages and disadvantages in the clinical context. The health care professional and caregiver cluster contains two themes: collaboration among patients, health care professionals, and caregivers, and changes in the roles of patients and professionals. Moreover, we classified the future research suggestions given in the literature into 5 directions in terms of research designs and research topics. Finally, based on our reflections on the observations from the review, we suggest four future research directions: (1) users' cognitions and emotions related to processing and interpreting the information produced by tracking devices and apps; (2) the dark side of self-tracking (eg, its adverse psychosocial consequences); (3) self-tracking as a societal phenomenon; and (4) systemic impacts of self-tracking on health care and the actors involved.

CONCLUSIONS

This systematic literature review contributes to research and practice by assisting future research activities and providing practitioners with a concise overview of the state of the art of self-tracking and the quantified self.

Inertial measurement units and application for remote healthcare in hip and knee osteoarthritis: a narrative review (Preprint)

Background

Measuring and modifying movement-related joint loading is integral to the management of lower extremity osteoarthritis (OA). Although traditional approaches rely on measurements made within the laboratory or clinical environments, inertial sensors provide an opportunity to quantify these outcomes in patients’ natural environments, providing greater ecological validity and opportunities to develop large data sets of movement data for the development of OA interventions.

Objective

This narrative review aimed to discuss and summarize recent developments in the use of inertial sensors for assessing movement during daily activities in individuals with hip and knee OA and to identify how this may translate to improved remote health care for this population.

Methods

A literature search was performed in November 2018 and repeated in July 2019 and March 2021 using the PubMed and Embase databases for publications on inertial sensors in hip and knee OA published in English within the previous 5 years. The search terms encompassed both OA and wearable sensors. Duplicate studies, systematic reviews, conference abstracts, and study protocols were also excluded. One reviewer screened the search result titles by removing irrelevant studies, and 2 reviewers screened study abstracts to identify studies using inertial sensors as the main sensing technology and a primary outcome related to movement quality. In addition, after the March 2021 search, 2 reviewers rescreened all previously included studies to confirm their relevance to this review.

Results

From the search process, 43 studies were determined to be relevant and subsequently included in this review. Inertial sensors have been successfully implemented for assessing the presence and severity of OA (n=11), assessing disease progression risk and providing feedback for gait retraining (n=7), and remotely monitoring intervention outcomes and identifying potential responders and nonresponders to interventions (n=14). In addition, studies have validated the use of inertial sensors for these applications (n=8) and analyzed the optimal sensor placement combinations and data input analysis for measuring different metrics of interest (n=3). These studies show promise for remote health care monitoring and intervention delivery in hip and knee OA, but many studies have focused on walking rather than a range of activities of daily living and have been performed in small samples (<100 participants) and in a laboratory rather than in a real-world environment.

Conclusions

Inertial sensors show promise for remote monitoring, risk assessment, and intervention delivery in individuals with hip and knee OA. Future opportunities remain to validate these sensors in real-world settings across a range of activities of daily living and to optimize sensor placement and data analysis approaches.

Electronic patient-reported outcome measures using mobile health technology in rheumatology: A scoping review

OBJECTIVE

This scoping review aims to characterize the current literature on electronic patient-reported outcome measures (ePROMs) in rheumatology and assess the feasibility and utility of ePROMs and mobile health technology in the management of rheumatic disease.

INTRODUCTION

Patient-reported outcome measures (PROMs) are commonly used in rheumatology as they are important markers of disease activity and overall function, encourage shared decision-making, and are associated with high rates of patient satisfaction. With the widespread use of mobile devices, there is increasing interest in the use of mobile health technology to collect electronic PROMs (ePROM).

INCLUSION CRITERIA

All primary studies that involve the collection of ePROMs using mobile devices by individuals with a rheumatic disease were included. Articles were excluded if ePROMs were measured during clinic appointments.

METHODS

A scoping review was performed using Medline, Embase, PsycINFO, and CINAHL with index terms and key words related to "patient-reported outcome measures", "rheumatic diseases", and "mobile health technology".

RESULTS

A total of 462 records were identified after duplicates were removed. Of the 70 studies selected for review, 43% were conference proceedings and 57% were journal articles, with the majority published in 2016 or later. Inflammatory arthritis was the most common rheumatic disease studied. Generic ePROMs were used over three times more often than disease-specific ePROMs. A total of 39 (56%) studies directly evaluated the feasibility of ePROMs in clinical practice, 19 (27%) were clinical trials that used ePROMs as study endpoints, 9 (13%) were focus groups or surveys on smartphone application development, and 3 (4%) did not fit into one defined category.

CONCLUSION

The use of ePROMs in rheumatology is a growing area of research and shows significant utility in clinical practice, particularly in inflammatory arthritis. Further research is needed to better characterize the feasibility of ePROMs in rheumatology and their impact on patient outcomes.

Satisfaction, Usability, and Compliance With the Use of Smartwatches for Ecological Momentary Assessment of Knee Osteoarthritis Symptoms in Older Adults: Usability Study

Background

Smartwatches enable physicians to monitor symptoms in patients with knee osteoarthritis, their behavior, and their environment. Older adults experience fluctuations in their pain and related symptoms (mood, fatigue, and sleep quality) that smartwatches are ideally suited to capture remotely in a convenient manner.

Objective

The aim of this study was to evaluate satisfaction, usability, and compliance using the real-time, online assessment and mobility monitoring (ROAMM) mobile app designed for smartwatches for individuals with knee osteoarthritis.

Methods

Participants (N=28; mean age 73.2, SD 5.5 years; 70% female) with reported knee osteoarthritis were asked to wear a smartwatch with the ROAMM app installed. They were prompted to report their prior night’s sleep quality in the morning, followed by ecological momentary assessments (EMAs) of their pain, fatigue, mood, and activity in the morning, afternoon, and evening. Satisfaction, comfort, and usability were evaluated using a standardized questionnaire. Compliance with regard to answering EMAs was calculated after excluding time when the watch was not being worn for technical reasons (eg, while charging).

Results

A majority of participants reported that the text displayed was large enough to read (22/26, 85%), and all participants found it easy to enter ratings using the smartwatch. Approximately half of the participants found the smartwatch to be comfortable (14/26, 54%) and would consider wearing it as their personal watch (11/24, 46%). Most participants were satisfied with its battery charging system (20/26, 77%). A majority of participants (19/26, 73%) expressed their willingness to use the ROAMM app for a 1-year research study. The overall EMA compliance rate was 83% (2505/3036 responses). The compliance rate was lower among those not regularly wearing a wristwatch (10/26, 88% vs 16/26, 71%) and among those who found the text too small to read (4/26, 86% vs 22/26, 60%).

Conclusions

Older adults with knee osteoarthritis positively rated the ROAMM smartwatch app and were generally satisfied with the device. The high compliance rates coupled with the willingness to participate in a long-term study suggest that the ROAMM app is a viable approach to remotely collecting health symptoms and behaviors for both research and clinical endeavors.

Monitoring work-related physical activity and estimating lower-limb loading: a proof-of-concept study

BACKGROUND

Physical activity (PA) is important to general health and knee osteoarthritis (OA). Excessive workplace PA is an established risk factor for knee OA however, appropriate methods of measurement are unclear. There is a need to examine and assess the utility of new methods of measuring workplace PA and estimating knee load prior to application to large-scale, knee OA cohorts. Our aims, therefore, were to monitor workplace PA and estimate lower-limb loading across different occupations in health participants.

METHODS

Twenty-four healthy adults, currently working full-time in a single occupation (≥ 35 h/week) and free of musculoskeletal disease, comorbidity and had no history of lower-limb injury/surgery (past 12-months) were recruited across New South Wales (Australia). A convenience sample was recruited with occupations assigned to levels of workload; sedentary, light manual and heavy manual. Metrics of workplace PA including tasks performed (i.e., sitting), step-count and lower-limb loading were monitored over 10 working days using a daily survey, smartwatch, and a smartphone.

RESULTS

Participants of light manual occupations had the greatest between-person variations in mean lower-limb load (from 2 to 59 kg*m/s3). Lower-limb load for most participants of the light manual group was similar to a single participant in heavy manual work (30 kg*m/s3) and was at least three times greater than the sedentary group (2 kg*m/s3). The trends of workplace PA over working hours were largely consistent, per individual, but rare events of extreme loads were observed across all participants (up to 760 kg*m/s3).

CONCLUSIONS

There are large interpersonal variations in metrics of workplace PA, particularly among light and heavy manual occupations. Our estimates of lower-limb loading were largely consistent with pre-conceived levels of physical demand. We present a new approach to monitoring PA and estimating lower-limb loading, which could be applied to future occupational studies of knee OA.

Framework for selecting and benchmarking mobile devices in psychophysiological research

Commercially available consumer electronics in (smartwatches and wearable biosensors) are increasingly enabling acquisition of peripheral physiological and physical activity data inside and outside of laboratory settings. However, there is scant literature available for selecting and assessing the suitability of these novel devices for scientific use. To overcome this limitation, the current paper offers a framework to aid researchers in choosing and evaluating wearable technologies for use in empirical research. Our seven-step framework includes: (1) identifying signals of interest; (2) characterizing intended use cases; (3) identifying study-specific pragmatic needs; (4) selecting devices for evaluation; (5) establishing an assessment procedure; (6) performing qualitative and quantitative analyses on resulting data; and, if desired, (7) conducting power analyses to determine sample size needed to more rigorously compare performance across devices. We illustrate the application of the framework by comparing electrodermal, cardiovascular, and accelerometry data from a variety of commercial wireless sensors (Affectiva Q, Empatica E3, Empatica E4, Actiwave Cardio, Shimmer) relative to a well-validated, wired MindWare laboratory system. Our evaluations are performed in two studies (N = 10, N = 11) involving psychometrically sound, standardized tasks that include physical activity and affect induction. After applying our framework to this data, we conclude that only some commercially available consumer devices for physiological measurement are capable of wirelessly measuring peripheral physiological and physical activity data of sufficient quality for scientific use cases. Thus, the framework appears to be beneficial at suggesting steps for conducting more systematic, transparent, and rigorous evaluations of mobile physiological devices prior to deployment in studies.

One Digital Health: A Unified Framework for Future Health Ecosystems

One Digital Health is a proposed unified structure. The conceptual framework of the One Digital Health Steering Wheel is built around two keys (ie, One Health and digital health), three perspectives (ie, individual health and well-being, population and society, and ecosystem), and five dimensions (ie, citizens’ engagement, education, environment, human and veterinary health care, and Healthcare Industry 4.0). One Digital Health aims to digitally transform future health ecosystems, by implementing a systemic health and life sciences approach that takes into account broad digital technology perspectives on human health, animal health, and the management of the surrounding environment. This approach allows for the examination of how future generations of health informaticians can address the intrinsic complexity of novel health and care scenarios in digitally transformed health ecosystems. In the emerging hybrid landscape, citizens and their health data have been called to play a central role in the management of individual-level and population-level perspective data. The main challenges of One Digital Health include facilitating and improving interactions between One Health and digital health communities, to allow for efficient interactions and the delivery of near–real-time, data-driven contributions in systems medicine and systems ecology. However, digital health literacy; the capacity to understand and engage in health prevention activities; self-management; and collaboration in the prevention, control, and alleviation of potential problems are necessary in systemic, ecosystem-driven public health and data science research. Therefore, people in a healthy One Digital Health ecosystem must use an active and forceful approach to prevent and manage health crises and disasters, such as the COVID-19 pandemic.

Toward dynamic phenotypes and the scalable measurement of human behavior

Precision psychiatry demands the rapid, efficient, and temporally dense collection of large scale and multi-omic data across diverse samples, for better diagnosis and treatment of dynamic clinical phenomena. To achieve this, we need approaches for measuring behavior that are readily scalable, both across participants and over time. Efforts to quantify behavior at scale are impeded by the fact that our methods for measuring human behavior are typically developed and validated for single time-point assessment, in highly controlled settings, and with relatively homogeneous samples. As a result, when taken to scale, these measures often suffer from poor reliability, generalizability, and participant engagement. In this review, we attempt to bridge the gap between gold standard behavioral measurements in the lab or clinic and the large-scale, high frequency assessments needed for precision psychiatry. To do this, we introduce and integrate two frameworks for the translation and validation of behavioral measurements. First, borrowing principles from computer science, we lay out an approach for iterative task development that can optimize behavioral measures based on psychometric, accessibility, and engagement criteria. Second, we advocate for a participatory research framework (e.g., citizen science) that can accelerate task development as well as make large-scale behavioral research more equitable and feasible. Finally, we suggest opportunities enabled by scalable behavioral research to move beyond single time-point assessment and toward dynamic models of behavior that more closely match clinical phenomena.

The Sensor-Based Physical Analogue Scale as a Novel Approach for Assessing Frequent and Fleeting Events: Proof of Concept

New technologies (e.g., smartphones) have made it easier to conduct Experience Sampling Method (ESM) studies and thereby collect longitudinal data in situ. However, limiting interruption burden (i.e., the strain of being pulled out of everyday life) remains a challenge, especially when assessments are frequent and/or must be made immediately after an event, such as when capturing the severity of clinical symptoms in everyday life. Here, we describe a wrist-worn microcomputer programmed with a Physical Analogue Scale (PAS) as a novel approach to ESM in everyday life. The PAS uses the position of a participant's forearm between flat and fully upright as a response scale like a Visual Analogue Scale (VAS) uses continuous ratings on a horizontal line. We present data from two pilot studies (4-week field study and lab study) and data from a 2-week ESM study on social media ostracism (i.e., when one's social media message is ignored; N = 53 participants and 2,272 event- and time-based assessments) to demonstrate the feasibility of this novel approach for event- and time-based assessments, and highlight advantages of our approach. PAS angles were accurate and reliable, and VAS and PAS values were highly correlated. Furthermore, we replicated past research on cyber ostracism, by finding that being ignored resulted in significantly stronger feelings of being offended, which was more pronounced when ignored by a group compared to a single person. Furthermore, participants did not find it overly difficult to complete the assessments using the wearable and the PAS. We suggest that the PAS is a valid measurement procedure in order to assess fleeting and/or frequent micro-situations in everyday life. The source code and administration application are freely available.