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Mobile Phone-Based Unobtrusive Ecological Momentary Assessment of Day-to-Day Mood: An Explorative Study.

Asselbergs J, Ruwaard J, Ejdys M, Schrader N, Sijbrandij M, Riper H - J. Med. Internet Res. (2016)

Bottom Line: However, the predictive performance of these models was significantly inferior to that of naive benchmark models.At present, we do not recommend the application of FSR-based mood prediction in real-world clinical settings.Further psychometric studies and more advanced data mining techniques are needed to unlock unobtrusive EMA's true potential.

View Article: PubMed Central - HTML - PubMed

Affiliation: Faculty of Behavioural and Movement Sciences, Section Clinical Psychology, Vrije Universiteit Amsterdam, Amsterdam, Netherlands. j.a.g.j.asselbergs@vu.nl.

ABSTRACT

Background: Ecological momentary assessment (EMA) is a useful method to tap the dynamics of psychological and behavioral phenomena in real-world contexts. However, the response burden of (self-report) EMA limits its clinical utility.

Objective: The aim was to explore mobile phone-based unobtrusive EMA, in which mobile phone usage logs are considered as proxy measures of clinically relevant user states and contexts.

Methods: This was an uncontrolled explorative pilot study. Our study consisted of 6 weeks of EMA/unobtrusive EMA data collection in a Dutch student population (N=33), followed by a regression modeling analysis. Participants self-monitored their mood on their mobile phone (EMA) with a one-dimensional mood measure (1 to 10) and a two-dimensional circumplex measure (arousal/valence, -2 to 2). Meanwhile, with participants' consent, a mobile phone app unobtrusively collected (meta) data from six smartphone sensor logs (unobtrusive EMA: calls/short message service (SMS) text messages, screen time, application usage, accelerometer, and phone camera events). Through forward stepwise regression (FSR), we built personalized regression models from the unobtrusive EMA variables to predict day-to-day variation in EMA mood ratings. The predictive performance of these models (ie, cross-validated mean squared error and percentage of correct predictions) was compared to naive benchmark regression models (the mean model and a lag-2 history model).

Results: A total of 27 participants (81%) provided a mean 35.5 days (SD 3.8) of valid EMA/unobtrusive EMA data. The FSR models accurately predicted 55% to 76% of EMA mood scores. However, the predictive performance of these models was significantly inferior to that of naive benchmark models.

Conclusions: Mobile phone-based unobtrusive EMA is a technically feasible and potentially powerful EMA variant. The method is young and positive findings may not replicate. At present, we do not recommend the application of FSR-based mood prediction in real-world clinical settings. Further psychometric studies and more advanced data mining techniques are needed to unlock unobtrusive EMA's true potential.

No MeSH data available.


Related in: MedlinePlus

Predictive performance (mean squared error and % correct predictions) of the personalized models as observed for the prediction of the one-dimensional EMA mood measure for each participant (N=27) during cross-validated forward selection regression (stepCV).
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figure4: Predictive performance (mean squared error and % correct predictions) of the personalized models as observed for the prediction of the one-dimensional EMA mood measure for each participant (N=27) during cross-validated forward selection regression (stepCV).

Mentions: Figure 4 shows the development of the cross-validated MSE and the percentage of correct responses during the stepCV training process, in which variables were sequentially added to the personalized model for each participant. With 42 days of training data, up to eight variables were selected (ie, the preset maximum of variables was reached). Governed by the algorithm, the MSE gradually decreased with each added variable. With regard to the correct predictions, the percentages tended to increase with increasing model complexity as well, but not continuously and not for each participant. This was expected because the percentage of correct predictions was not a parameter in the model optimization process.


Mobile Phone-Based Unobtrusive Ecological Momentary Assessment of Day-to-Day Mood: An Explorative Study.

Asselbergs J, Ruwaard J, Ejdys M, Schrader N, Sijbrandij M, Riper H - J. Med. Internet Res. (2016)

Predictive performance (mean squared error and % correct predictions) of the personalized models as observed for the prediction of the one-dimensional EMA mood measure for each participant (N=27) during cross-validated forward selection regression (stepCV).
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4829730&req=5

figure4: Predictive performance (mean squared error and % correct predictions) of the personalized models as observed for the prediction of the one-dimensional EMA mood measure for each participant (N=27) during cross-validated forward selection regression (stepCV).
Mentions: Figure 4 shows the development of the cross-validated MSE and the percentage of correct responses during the stepCV training process, in which variables were sequentially added to the personalized model for each participant. With 42 days of training data, up to eight variables were selected (ie, the preset maximum of variables was reached). Governed by the algorithm, the MSE gradually decreased with each added variable. With regard to the correct predictions, the percentages tended to increase with increasing model complexity as well, but not continuously and not for each participant. This was expected because the percentage of correct predictions was not a parameter in the model optimization process.

Bottom Line: However, the predictive performance of these models was significantly inferior to that of naive benchmark models.At present, we do not recommend the application of FSR-based mood prediction in real-world clinical settings.Further psychometric studies and more advanced data mining techniques are needed to unlock unobtrusive EMA's true potential.

View Article: PubMed Central - HTML - PubMed

Affiliation: Faculty of Behavioural and Movement Sciences, Section Clinical Psychology, Vrije Universiteit Amsterdam, Amsterdam, Netherlands. j.a.g.j.asselbergs@vu.nl.

ABSTRACT

Background: Ecological momentary assessment (EMA) is a useful method to tap the dynamics of psychological and behavioral phenomena in real-world contexts. However, the response burden of (self-report) EMA limits its clinical utility.

Objective: The aim was to explore mobile phone-based unobtrusive EMA, in which mobile phone usage logs are considered as proxy measures of clinically relevant user states and contexts.

Methods: This was an uncontrolled explorative pilot study. Our study consisted of 6 weeks of EMA/unobtrusive EMA data collection in a Dutch student population (N=33), followed by a regression modeling analysis. Participants self-monitored their mood on their mobile phone (EMA) with a one-dimensional mood measure (1 to 10) and a two-dimensional circumplex measure (arousal/valence, -2 to 2). Meanwhile, with participants' consent, a mobile phone app unobtrusively collected (meta) data from six smartphone sensor logs (unobtrusive EMA: calls/short message service (SMS) text messages, screen time, application usage, accelerometer, and phone camera events). Through forward stepwise regression (FSR), we built personalized regression models from the unobtrusive EMA variables to predict day-to-day variation in EMA mood ratings. The predictive performance of these models (ie, cross-validated mean squared error and percentage of correct predictions) was compared to naive benchmark regression models (the mean model and a lag-2 history model).

Results: A total of 27 participants (81%) provided a mean 35.5 days (SD 3.8) of valid EMA/unobtrusive EMA data. The FSR models accurately predicted 55% to 76% of EMA mood scores. However, the predictive performance of these models was significantly inferior to that of naive benchmark models.

Conclusions: Mobile phone-based unobtrusive EMA is a technically feasible and potentially powerful EMA variant. The method is young and positive findings may not replicate. At present, we do not recommend the application of FSR-based mood prediction in real-world clinical settings. Further psychometric studies and more advanced data mining techniques are needed to unlock unobtrusive EMA's true potential.

No MeSH data available.


Related in: MedlinePlus