Background: Despite the growing interest in the experience sampling method (ESM) as a data collection tool for mental health research, the absence of methodological guidelines related to its use has resulted in a large heterogeneity of designs. Concomitantly, the potential effects of the design on the response behavior of the participants remain largely unknown. Objective: The objective of this meta-analysis was to investigate the associations between various sample and design characteristics and the compliance and retention rates of studies using ESM in mental health research. Methods: ESM studies investigating major depressive disorder, bipolar disorder, and psychotic disorder were considered for inclusion. Besides the compliance and retention rates, a number of sample and design characteristics of the selected studies were collected to assess their potential relationships with the compliance and retention rates. Multilevel random/mixed effects models were used for the analyses. Results: Compliance and retention rates were lower for studies with a higher proportion of male participants (P<.001) and individuals with a psychotic disorder (P<.001). Compliance was positively associated with the use of a fixed sampling scheme (P=.02), higher incentives (P=.03), higher time intervals between successive evaluations (P=.02), and fewer evaluations per day (P=.008), while no significant associations were observed with regard to the mean age of the sample, the study duration, or other design characteristics. Conclusions: The findings demonstrate that ESM studies can be carried out in mental health research, but the quality of the data collection depends upon a number of factors related to the design of ESM studies and the samples under study that need to be considered when designing such protocols.

Background: In the absence of a vaccine or effective treatment for COVID-19, countries have adopted nonpharmaceutical interventions (NPIs) such as social distancing and full lockdown. An objective and quantitative means of passively monitoring the impact and response of these interventions at a local level is needed. Objective: We aim to explore the utility of the recently developed open-source mobile health platform Remote Assessment of Disease and Relapse (RADAR)–base as a toolbox to rapidly test the effect and response to NPIs intended to limit the spread of COVID-19. Methods: We analyzed data extracted from smartphone and wearable devices, and managed by the RADAR-base from 1062 participants recruited in Italy, Spain, Denmark, the United Kingdom, and the Netherlands. We derived nine features on a daily basis including time spent at home, maximum distance travelled from home, the maximum number of Bluetooth-enabled nearby devices (as a proxy for physical distancing), step count, average heart rate, sleep duration, bedtime, phone unlock duration, and social app use duration. We performed Kruskal-Wallis tests followed by post hoc Dunn tests to assess differences in these features among baseline, prelockdown, and during lockdown periods. We also studied behavioral differences by age, gender, BMI, and educational background. Results: We were able to quantify expected changes in time spent at home, distance travelled, and the number of nearby Bluetooth-enabled devices between prelockdown and during lockdown periods (P<.001 for all five countries). We saw reduced sociality as measured through mobility features and increased virtual sociality through phone use. People were more active on their phones (P<.001 for Italy, Spain, and the United Kingdom), spending more time using social media apps (P<.001 for Italy, Spain, the United Kingdom, and the Netherlands), particularly around major news events. Furthermore, participants had a lower heart rate (P<.001 for Italy and Spain; P=.02 for Denmark), went to bed later (P<.001 for Italy, Spain, the United Kingdom, and the Netherlands), and slept more (P<.001 for Italy, Spain, and the United Kingdom). We also found that young people had longer homestay than older people during the lockdown and fewer daily steps. Although there was no significant difference between the high and low BMI groups in time spent at home, the low BMI group walked more. Conclusions: RADAR-base, a freely deployable data collection platform leveraging data from wearables and mobile technologies, can be used to rapidly quantify and provide a holistic view of behavioral changes in response to public health interventions as a result of infectious outbreaks such as COVID-19. RADAR-base may be a viable approach to implementing an early warning system for passively assessing the local compliance to interventions in epidemics and pandemics, and could help countries ease out of lockdown.

Intensive repeated measurement techniques, such as the experience sampling method (ESM), put high demands on participants and may lead to low response compliance, which, in turn, may affect data quality. Therefore, the objective of this study was to investigate ESM compliance and predictors thereof based on a pooled dataset of 10 ESM studies with a total of 92,394 momentary assessments from 1,717 individuals with different mental health conditions. All included studies used an ESM paper-and-pencil diary protocol of 4 to 6 study days with 10 random time assessments per day. Analyses were conducted using multilevel mixed-effects logistic regression models. Results indicated overall acceptable compliance with an average response rate of 78% (95% CI [0.74, 0.82]). However, compliance declined across days (p < .001), reaching a low on the 5th day with 73% (95% CI [0.68, 0.77]). Compliance also varied significantly across assessments depending on the time within a day (p < .001), with highest compliance between 12 p.m. and 1.30 p.m. (83%; 95% CI [0.80, 0.86]) and lowest compliance between 7.30 a.m. and 9 a.m. (56%; 95% CI [0.50, 0.62]). Persons with psychosis were less compliant than healthy participants (70% vs. 83%, respectively; p < .001). Also females (p = .002) and older participants (p < .001) were slightly more compliant. The findings suggest acceptable compliance in an ESM protocol of 4 to 6 study days with a high frequency of 10 assessments per day despite fluctuations across and within study days. Further evidence on compliance and its predictors in different ESM protocols is needed, especially in clinical populations. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

There is a growing body of literature highlighting the role that wearable and mobile remote measurement technology (RMT) can play in measuring symptoms of major depressive disorder (MDD). Outcomes assessment typically relies on self-report, which can be biased by dysfunctional perceptions and current symptom severity. Predictors of depressive relapse include disrupted sleep, reduced sociability, physical activity, changes in mood, prosody and cognitive function, which are all amenable to measurement via RMT. This study aims to: 1) determine the usability, feasibility and acceptability of RMT; 2) improve and refine clinical outcome measurement using RMT to identify current clinical state; 3) determine whether RMT can provide information predictive of depressive relapse and other critical outcomes. RADAR-MDD is a multi-site prospective cohort study, aiming to recruit 600 participants with a history of depressive disorder across three sites: London, Amsterdam and Barcelona. Participants will be asked to wear a wrist-worn activity tracker and download several apps onto their smartphones. These apps will be used to either collect data passively from existing smartphone sensors, or to deliver questionnaires, cognitive tasks, and speech assessments. The wearable device, smartphone sensors and questionnaires will collect data for up to 2-years about participants’ sleep, physical activity, stress, mood, sociability, speech patterns, and cognitive function. The primary outcome of interest is MDD relapse, defined via the Inventory of Depressive Symptomatology- Self-Report questionnaire (IDS-SR) and the World Health Organisation’s self-reported Composite International Diagnostic Interview (CIDI-SF). This study aims to provide insight into the early predictors of major depressive relapse, measured unobtrusively via RMT. If found to be acceptable to patients and other key stakeholders and able to provide clinically useful information predictive of future deterioration, RMT has potential to change the way in which depression and other long-term conditions are measured and managed.

COVID-19 has abruptly disrupted healthcare services; however, the continuity of rehabilitation could be guaranteed using mobile technologies. This review aims to analyze the feasibility and effectiveness of telehealth solutions proposed to guarantee the continuity of rehabilitation during the COVID-19 pandemic. The PubMed, Cochrane Library, Web of Science and PEDro databases were searched; the search was limited to randomized controlled trials, observational and explorative studies published up to 31 May 2022, assessing the feasibility and effectiveness of telerehabilitation during the COVID-19 pandemic. Twenty studies were included, for a total of 224,806 subjects: 93.1% with orthopedic complaints and 6.9% with non-orthopedic ones. The main strategies used were video and audio calls via commonly available technologies and free videoconferencing tools. Based on the current evidence, it is suggested that telerehabilitation is a feasible and effective solution, allowing the continuity of rehabilitation while reducing the risk of infection and the burden of travel. However, it is not widely used in clinical settings, and definitive conclusions cannot be currently drawn. Telerehabilitation seems a feasible and safe option to remotely deliver rehabilitation using commonly available mobile technologies, guaranteeing the continuity of care while respecting social distancing. Further research is, however, needed to strengthen and confirm these findings.