An Automated Mobile Mood Tracking Technology (Mood 24/7): Validation Study

Kumar, Anupama; Wang, Michael; Riehm, Alison; Yu, Eileen; Smith, Theodore; Kaplin, Adam I.

doi:10.2196/16237

Cited by 7 publications

(6 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The authors have shown in comparative studies that significantly higher adherence rates can be achieved with

EMA than with conventional EMA [ 24 , 40 ]. Although we observed only a relatively small number of

EMA studies (1.5%), in our meta-analysis we found significantly higher adherence rates in these studies compared to the remaining studies, confirming the findings of Intille et al Thus, even

EMA studies with up to 72 prompts per day [ 41 ] or 36 weeks study duration [ 42 ] achieved high adherence rates of 74–97%. In consequence,

EMA may be used to collect self-report measurements with high temporal density while reducing the burden on participants [ 40 ].…”

Section: Discussionsupporting

confidence: 90%

Mobile Crowdsensing in Ecological Momentary Assessment mHealth Studies: A Systematic Review and Analysis

Kraft,

Reichert,

Pryss

2024

Sensors

View full text Add to dashboard Cite

As mobile devices have become a central part of our daily lives, they are also becoming increasingly important in research. In the medical context, for example, smartphones are used to collect ecologically valid and longitudinal data using Ecological Momentary Assessment (EMA), which is mostly implemented through questionnaires delivered via smart notifications. This type of data collection is intended to capture a patient’s condition on a moment-to-moment and longer-term basis. To collect more objective and contextual data and to understand patients even better, researchers can not only use patients’ input via EMA, but also use sensors as part of the Mobile Crowdsensing (MCS) approach. In this paper, we examine how researchers have embraced the topic of MCS in the context of EMA through a systematic literature review. This PRISMA-guided review is based on the databases PubMed, Web of Science, and EBSCOhost. It is shown through the results that both EMA research in general and the use of sensors in EMA research are steadily increasing. In addition, most of the studies reviewed used mobile apps to deliver EMA to participants, used a fixed-time prompting strategy, and used signal-contingent or interval-contingent self-assessment as sampling/assessment strategies. The most commonly used sensors in EMA studies are the accelerometer and GPS. In most studies, these sensors are used for simple data collection, but sensor data are also commonly used to verify study participant responses and, less commonly, to trigger EMA prompts. Security and privacy aspects are addressed in only a subset of mHealth EMA publications. Moreover, we found that EMA adherence was negatively correlated with the total number of prompts and was higher in studies using a microinteraction-based EMA (μEMA) approach as well as in studies utilizing sensors. Overall, we envision that the potential of the technological capabilities of smartphones and sensors could be better exploited in future, more automated approaches.

show abstract

“…The authors have shown in comparative studies that significantly higher adherence rates can be achieved with

EMA than with conventional EMA [ 24 , 40 ]. Although we observed only a relatively small number of

EMA studies (1.5%), in our meta-analysis we found significantly higher adherence rates in these studies compared to the remaining studies, confirming the findings of Intille et al Thus, even

EMA studies with up to 72 prompts per day [ 41 ] or 36 weeks study duration [ 42 ] achieved high adherence rates of 74–97%. In consequence,

EMA may be used to collect self-report measurements with high temporal density while reducing the burden on participants [ 40 ].…”

Section: Discussionsupporting

confidence: 90%

Mobile Crowdsensing in Ecological Momentary Assessment mHealth Studies: A Systematic Review and Analysis

Kraft,

Reichert,

Pryss

2024

Sensors

View full text Add to dashboard Cite

show abstract

“…41 Other digital surveys have similarly demonstrated user engagement. 2,32,34,35 To summarize the main findings from our prior work, we found that participants with BP adhered to recording at least half of their symptoms (6 out of 12) within the app for an average (95% CI) of 81.8% (73.1%-90.4%) days in the study. Over 6 weeks, we estimated that these adherence rates declined by about 6.1% over the study.…”

Section: Longitudinal Analysismentioning

confidence: 82%

“…21,23 Current digital surveys of mood such as the ChronoRecord, NIMH Life-Chart-Method, or Mood 24/7 place mood on a one-dimensional scale. 32,34,35 A onedimensional scale, however, cannot capture mixed states, that is, times when both depressive and manic symptoms manifest.…”

Section: Longitudinal Analysismentioning

confidence: 99%

A digital self‐report survey of mood for bipolar disorder

et al. 2021

View full text Add to dashboard Cite

Research conducted digitally is a growing part of how we study bipolar disorder (BP). This fact is underscored by the present risk of in-person interviews due to Covid-19. Digital research offers many opportunities beyond providing an alternative to in-person data collection. The most obvious are mobile apps to manage mood and deliver treatment for BP. [1][2][3][4][5][6] Other opportunities include the ability to build BP cohorts not limited by geography and to phenotype BP at finer time scales [7][8][9][10][11] with greater, inthe-moment context on an individual's life including their physiology. [12][13][14][15][16][17] To improve the successes of digital research, the community is looking for standardized reporting tools for BP symptoms in a digital setting. [18][19][20] However, there are reasons, which we discuss below, why current tools being used in digital settings may not always be satisfactory. Thus, the present paper seeks to establish a new digital self-report survey for measuring mood in BP, which we call digiBP.

show abstract

“…Some prior studies support the feasibility of monitoring patient mood longitudinally in people living with MS as well as the possible effectiveness of a closed-loop approach. There is good concordance between patient reports and clinical depression, supporting the use of patient-reported tools for depression monitoring [15]. The 3-month pilot CoachMS randomized controlled trial (RCT; NCT03335618; n=21; people living with MS; [16]) showed that it was feasible, and acceptable, to monitor patients' bothersome symptoms (mood, ambulation, and bladder) and to act on them clinically in near real time (coaching patients to address these) [16].…”

Section: Introductionmentioning

confidence: 89%

A Closed-Loop Digital Health Tool to Improve Depression Care in Multiple Sclerosis: Iterative Design and Cross-Sectional Pilot Randomized Controlled Trial and its Impact on Depression Care

Henderson,

Reihm,

Koshal

et al. 2024

JMIR Form Res

View full text Add to dashboard Cite

Background People living with multiple sclerosis (MS) face a higher likelihood of being diagnosed with a depressive disorder than the general population. Although many low-cost screening tools and evidence-based interventions exist, depression in people living with MS is underreported, underascertained by clinicians, and undertreated. Objective This study aims to design a closed-loop tool to improve depression care for these patients. It would support regular depression screening, tie into the point of care, and support shared decision-making and comprehensive follow-up. After an initial development phase, this study involved a proof-of-concept pilot randomized controlled trial (RCT) validation phase and a detailed human-centered design (HCD) phase. Methods During the initial development phase, the technological infrastructure of a clinician-facing point-of-care clinical dashboard for MS management (BRIDGE) was leveraged to incorporate features that would support depression screening and comprehensive care (Care Technology to Ascertain, Treat, and Engage the Community to Heal Depression in people living with MS [MS CATCH]). This linked a patient survey, in-basket messages, and a clinician dashboard. During the pilot RCT phase, a convenience sample of 50 adults with MS was recruited from a single MS center with 9-item Patient Health Questionnaire scores of 5-19 (mild to moderately severe depression). During the routine MS visit, their clinicians were either asked or not to use MS CATCH to review their scores and care outcomes were collected. During the HCD phase, the MS CATCH components were iteratively modified based on feedback from stakeholders: people living with MS, MS clinicians, and interprofessional experts. Results MS CATCH links 3 features designed to support mood reporting and ascertainment, comprehensive evidence-based management, and clinician and patient self-management behaviors likely to lead to sustained depression relief. In the pilot RCT (n=50 visits), visits in which the clinician was randomized to use MS CATCH had more notes documenting a discussion of depressive symptoms than those in which MS CATCH was not used (75% vs 34.6%; χ21=8.2; P=.004). During the HCD phase, 45 people living with MS, clinicians, and other experts participated in the design and refinement. The final testing round included 20 people living with MS and 10 clinicians including 5 not affiliated with our health system. Most scoring targets for likeability and usability, including perceived ease of use and perceived effectiveness, were met. Net Promoter Scale was 50 for patients and 40 for clinicians. Conclusions Created with extensive stakeholder feedback, MS CATCH is a closed-loop system aimed to increase communication about depression between people living with MS and their clinicians, and ultimately improve depression care. The pilot findings showed evidence of enhanced communication. Stakeholders also advised on trial design features of a full year long Department of Defense–funded feasibility and efficacy trial, which is now underway. Trial Registration ClinicalTrials.gov NCT05865405; http://tinyurl.com/4zkvru9x

show abstract

An Automated Mobile Mood Tracking Technology (Mood 24/7): Validation Study

Cited by 7 publications

References 18 publications

Mobile Crowdsensing in Ecological Momentary Assessment mHealth Studies: A Systematic Review and Analysis

Mobile Crowdsensing in Ecological Momentary Assessment mHealth Studies: A Systematic Review and Analysis

A digital self‐report survey of mood for bipolar disorder

A Closed-Loop Digital Health Tool to Improve Depression Care in Multiple Sclerosis: Iterative Design and Cross-Sectional Pilot Randomized Controlled Trial and its Impact on Depression Care

Contact Info

Product

Resources

About