Dataset Information

A comparative study of engagement in mobile and wearable health monitoring for bipolar disorder.

ABSTRACT:

Objectives

Self-monitoring is recommended for individuals with bipolar disorder, with numerous technological solutions available. This study aimed to identify basic components of these solutions that increase engagement with self-monitoring.

Methods

Participants with bipolar disorder (n = 47) monitored their symptoms with a Fitbit and a smartphone app and were randomly assigned to either review or not review recorded symptoms weekly. We tested whether individuals would better adhere to and prefer monitoring with passive monitoring with an activity tracker compared to active monitoring with a smartphone app and whether individuals would better adhere to self-monitoring if their recorded symptoms were reviewed with an interviewer.

Results

Monitoring with a smartphone app achieved similar adherence and preference to Fitbit (P > .85). Linear mixed effects modeling found adherence decreased significantly more over the study for the Fitbit (12% more, P < .001) even though more participants reported they would use the Fitbit over a year compared to the app (72.3% vs 46.8%). Reviewing symptoms weekly did not improve adherence, but most participants reported they would prefer to review symptoms with a clinician (74.5%) and on monthly basis (57.5%) compared to alternatives. Participants endorsed sleep as the most important symptom to monitor, forgetfulness as the largest barrier to self-monitoring, and raising self-awareness as the best reason for self-monitoring.

Conclusions

We recommend a combined strategy of wearable and mobile monitoring that includes reminders, targets raising self-awareness, and tracks sleep. A clinician may want to review symptoms on a monthly basis.

Trial registration

ClinicalTrials.gov NCT03358238.

SUBMITTER: Van Til K

PROVIDER: S-EPMC7085979 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A comparative study of engagement in mobile and wearable health monitoring for bipolar disorder.

Van Til Kaela K McInnis Melvin G MG Cochran Amy A

Bipolar disorders 20191025 2

<h4>Objectives</h4>Self-monitoring is recommended for individuals with bipolar disorder, with numerous technological solutions available. This study aimed to identify basic components of these solutions that increase engagement with self-monitoring.<h4>Methods</h4>Participants with bipolar disorder (n = 47) monitored their symptoms with a Fitbit and a smartphone app and were randomly assigned to either review or not review recorded symptoms weekly. We tested whether individuals would better adhe ...[more]

PMID: 31610074

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Project description:BACKGROUND:Monitoring signs and symptoms in bipolar disorder (BP) is typically based on regular assessments from patient-clinician interactions. Mobile and wearable technology promises to make monitoring symptoms in BP easier, but little is known about how best to engage individuals with BP in monitoring symptoms. OBJECTIVE:The objective of this study was to provide the rationale and protocol for a randomized controlled trial that investigates engagement strategies for monitoring symptoms of BP, including the strategies of using activity trackers compared with self-reports and reviewing recorded symptoms weekly with an interviewer. METHODS:A total of 50 individuals with BP will be recruited from the Prechter Longitudinal Study of Bipolar Disorder at the University of Michigan to participate in a 6-week study. Participants will monitor their symptoms through an activity tracker (Fitbit Alta HR) and a mobile phone app designed for this study. In addition to monitoring symptoms, participants have a 50-50 chance of being assigned to an arm that reviews self-reports and activity information weekly. Statistical tests will be performed to test hypotheses that participants adhere to activity tracking significantly more than self-reporting, prefer activity tracking significantly more than self-reporting, and better adhere to both activity tracking and self-reporting when reviewing collected information weekly. RESULTS:Recruitment commenced in November 2017. The first group of participants began the study in January 2018. CONCLUSIONS:This study aims to establish strategies to engage individuals with BP in monitoring their symptoms with mobile and wearable technology. Better engagement strategies are expected to aid current efforts in bipolar research and clinical care, from the development of new mobile phone apps to providing the right intervention to the right individual at the right moment. TRIAL REGISTRATION:ClinicalTrials.gov NCT03358238; https://clinicaltrials.gov/ct2/show/NCT03358238 (Archived by WebCite at http://www.webcitation.org/6yebuNfz5). REGISTERED REPORT IDENTIFIER:RR1-10.2196/9899.

Project description:BackgroundBipolar disorder is a chronic, progressive illness characterized by recurrent episodes of mania and depression. Self-report scales have historically played a significant role in the monitoring of bipolar symptoms. However, these tools rely on episodic memory, which can be unreliable and do not allow the clinician to monitor brief episodic symptoms or the course of symptoms over shorter periods of time. Mobile app-based questionnaires have been suggested as a tool to improve monitoring of patients with bipolar disorder.ObjectiveThis paper aims to determine the feasibility and validity of mobile app-based self-report questionnaires.MethodsWe performed a systematic review of the literature according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. The PubMed, PsycInfo, Web of Science, Ovid MEDLINE, and EMBASE databases were searched for papers published in English that assessed adherence to and the validity of mobile app-based self-report questionnaires. Relevant studies published from database creation to May 22, 2020, were identified, and results examining the validity of and rates of adherence to app-based self-report questionnaires are reported.ResultsA total of 13 records were identified for inclusion in this review. Of these studies, 4 assessed the concurrent validity of mobile app-based self-report tools, with the majority of findings indicating significant associations between data collected using these tools and the Young Mania Rating Scale, Hamilton Depression Rating Scale-17, or Montgomery-Åsberg Depression Rating Scale (P<.001 to P=.24). Three studies comparing the variability or range of symptoms between patients with bipolar disorder and healthy controls suggested that these data are capable of differentiating between known groups. Two studies demonstrated statistically significant associations between data collected via mobile app-based self-report tools and instruments assessing other clinically important factors. Adherence rates varied across the studies examined. However, good adherence rates (>70%) were observed in all but 1 study using a once-daily assessment. There was a wide range of adherence rates observed in studies using twice-daily assessments (42%-95%).ConclusionsThese findings suggest that mobile app-based self-report tools are valid in the assessment of symptoms of mania and depression in euthymic patients with bipolar disorder. Data collected using these tools appear to differ between patients with bipolar disorder and healthy controls and are significantly associated with other clinically important measures. It is unclear at this time whether these tools can be used to detect acute episodes of mania or depression in patients with bipolar disorder. Adherence data indicate that patients with bipolar disorder show good adherence to self-report assessments administered daily for the duration of the study periods evaluated.

Project description:Background: While preliminary evidence suggests that sensors may be employed to detect presence of low mood it is still unclear whether they can be leveraged for measuring depression symptom severity. This study evaluates the feasibility and performance of assessing depressive symptom severity by using behavioral and physiological features obtained from wristband and smartphone sensors. Method: Participants were thirty-one individuals with Major Depressive Disorder (MDD). The protocol included 8 weeks of behavioral and physiological monitoring through smartphone and wristband sensors and six in-person clinical interviews during which depression was assessed with the 17-item Hamilton Depression Rating Scale (HDRS-17). Results: Participants wore the right and left wrist sensors 92 and 94% of the time respectively. Three machine-learning models estimating depressive symptom severity were developed-one combining features from smartphone and wearable sensors, one including only features from the smartphones, and one including features from wrist sensors-and evaluated in two different scenarios. Correlations between the models' estimate of HDRS scores and clinician-rated HDRS ranged from moderate to high (0.46 [CI: 0.42, 0.74] to 0.7 [CI: 0.66, 0.74]) and had moderate accuracy with Mean Absolute Error ranging between 3.88 ± 0.18 and 4.74 ± 1.24. The time-split scenario of the model including only features from the smartphones performed the best. The ten most predictive features in the model combining physiological and mobile features were related to mobile phone engagement, activity level, skin conductance, and heart rate variability. Conclusion: Monitoring of MDD patients through smartphones and wrist sensors following a clinician-rated HDRS assessment is feasible and may provide an estimate of changes in depressive symptom severity. Future studies should further examine the best features to estimate depressive symptoms and strategies to further enhance accuracy.

Dataset Information

A comparative study of engagement in mobile and wearable health monitoring for bipolar disorder.

Objectives

Methods

Results

Conclusions

Trial registration

Publications

A comparative study of engagement in mobile and wearable health monitoring for bipolar disorder.

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