Tracking and Predicting Depressive Symptoms of Adolescents Using Smartphone-Based Self-Reports, Parental Evaluations, and Passive Phone Sensor Data: Development and Usability Study (Preprint)
Open accessAbstract
BACKGROUND: Depression carries significant financial, medical, and emotional burden on modern society. Various proof-of-concept studies have highlighted how apps can link dynamic mental health status changes to fluctuations in smartphone usage in adult patients with major depressive disorder (MDD). However, the use of such apps to monitor adolescents remains a challenge. OBJECTIVE: This study aimed to investigate whether smartphone apps are useful in evaluating and monitoring depression symptoms in a clinically depressed adolescent population compared with the following gold-standard clinical psychometric instruments: Patient Health Questionnaire (PHQ-9), Hamilton Rating Scale for Depression (HAM-D), and Hamilton Anxiety Rating Scale (HAM-A). METHODS: We recruited 13 families with adolescent patients diagnosed with MDD with or without comorbid anxiety disorder. Over an 8-week period, daily self-reported moods and smartphone sensor data were collected by using the Smartphone- and OnLine usage–based eValuation for Depression (SOLVD) app. The evaluations from teens’ parents were also collected. Baseline depression and anxiety symptoms were measured biweekly using PHQ-9, HAM-D, and HAM-A. RESULTS: We observed a significant correlation between the self-evaluated mood averaged over a 2-week period and the biweekly psychometric scores from PHQ-9, HAM-D, and HAM-A (0.45≤|r|≤0.63; P=.009, P=.01, and P=.003, respectively). The daily steps taken, SMS frequency, and average call duration were also highly correlated with clinical scores (0.44≤|r|≤0.72; all P<.05). By combining self-evaluations and smartphone sensor data of the teens, we could predict the PHQ-9 score with an accuracy of 88% (23.77/27). When adding the evaluations from the teens’ parents, the prediction accuracy was further increased to 90% (24.35/27). CONCLUSIONS: Smartphone apps such as SOLVD represent a useful way to monitor depressive symptoms in clinically depressed adolescents, and these apps correlate well with current gold-standard psychometric instruments. This is a first study of its kind that was conducted on the adolescent population, and it included inputs from both teens and their parents as observers. The results are preliminary because of the small sample size, and we plan to expand the study to a larger population.
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Written from the paper’s full text.
TL;DR
This first-of-its-kind SOLVD-Teen study found that smartphone-based self-reports, parental evaluations, and passive Android sensor data correlated with standard depression and anxiety scales in clinically depressed adolescents. Combining these data predicted PHQ-9 scores with reported accuracy of 88% from teen inputs plus sensors and 90% when parental evaluations were added.
Key findings
- Teens’ self-evaluated mood averaged over 2 weeks significantly correlated with biweekly PHQ-9, HAM-D, and HAM-A scores (0.45≤|r|≤0.63; P=.009, P=.01, and P=.003, respectively).
- Daily steps taken, SMS frequency, and average call duration were also highly correlated with clinical scores (0.44≤|r|≤0.72; all P<.05).
- Combining teen self-evaluations and smartphone sensor data predicted PHQ-9 with 88% accuracy (23.77/27); adding parental evaluations increased prediction accuracy to 90% (24.35/27).
- In the regression table, the lowest PHQ-9 prediction error was RMSE 2.65 using teens’ and parents’ inputs plus steps, GPS, SMS, and call data (P=.017).
Methods
- Recruited 13 families with adolescents diagnosed with MDD with or without comorbid anxiety disorder; teens were aged 12–17 years, and each had a participating parent.
- Over 8 weeks, daily self-reported moods and smartphone sensor/usage data were collected via the SOLVD-Teen and SOLVD-Parent Android apps; parental evaluations were also collected, and PHQ-9, HAM-D, and HAM-A were administered biweekly.
- Analysis used Pearson correlations between 2-week aggregated self/sensor data and biweekly psychometric scores, plus linear and polynomial-kernel support vector regressors to predict PHQ-9, with a 4:1 training/test split and RMSE evaluation.
Limitations
- The paper states the conclusion is preliminary due to the small sample size of 13 families; 8 of 13 families completed the entire 8-week trial, and recruitment ended because the AIM program closed.
- The paper states the study was constrained to Android phones because iOS did not permit continuous background tracking of location and smartphone usage data.
- Evident from the text: only 11 teens remained for analysis after 2 dropped out within 2 weeks, and the authors note no evidence that parents’ evaluations were biased by their own mental health status given the limited sample size.
Model-written summary. Check it against the paper before relying on it.
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Cites
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Claims the paper makes
0 of 12 cited sources checked · 0 supported · 0 flagged
- Findingown data
Daily steps taken, SMS frequency, and average call duration were highly correlated with clinical scores (0.44≤|r|≤0.72; all P<.05).
“The daily steps taken, SMS frequency, and average call duration were also highly correlated with clinical scores (0.44≤|r|≤0.72; all P<.05).”
Quote in the paperSubstantial claim - Findingown data
Adding evaluations from the teens' parents increased the prediction accuracy to 90% (24.35/27).
“When adding the evaluations from the teens’ parents, the prediction accuracy was further increased to 90% (24.35/27).”
- Also stated in: § Objective and Results · plain
“Third, if we combine parental reports with the teen’s smartphone sensor and usage data, then PHQ-9 can be predicted even more accurately.”
Quote in the paperSubstantial claim - Also stated in: § Objective and Results · plain
- Findingown data
Depressive symptoms assessed by clinical instruments were significantly correlated with mobility level and social interactions captured by passive smartphone sensor data.
“Depressive symptoms assessed by clinical instruments were also significantly correlated with mobility level and social interactions captured by passive smartphone sensor data.”
Quote in the paperSubstantial claim - Findingown data
Using only smartphone sensor and usage data yields an RMSE of 2.77 for PHQ-9 prediction.
“Conversely, if we use only smartphone sensor and usage data, the RMSE is 2.77.”
Quote in the paperSubstantial claim - Findingown data
Both teens' and parents' ratings for mood and anxiety level are significantly correlated with psychometric scores (PHQ-9, HAM-D, and HAM-A).
“From Figure 4, we observed that both teens’ and parents’ ratings for mood and anxiety level are significantly correlated with psychometric scores, and teens’ self-reports have a slightly higher correlation than parental inputs.”
- Also stated in: § Relationship Between Smartphone Data and Psychometric Scores · boosted
“Overall, the results indicate a strong correlation between daily smartphone-based EMA questions (ie, teens’ self-reports and parental inputs) and clinician-rated psychometric scores.”
- Also stated in: § Principal Findings · hedged
“Our study indicated that there were significant correlations between daily self-reported moods, parents’ evaluations, and clinical psychometric scores (PHQ-9, HAM-D, and HAM-A).”
Quote in the paperSubstantial claim - Also stated in: § Relationship Between Smartphone Data and Psychometric Scores · boosted
- Findingown data
A higher depression score is significantly correlated with lower social interaction level, such as shorter phone call durations and fewer text messages.
“The results indicate that a higher depression score is significantly correlated with lower social interaction level, such as shorter phone call durations and fewer text messages.”
Quote in the paperSubstantial claim - Findingown data
Teens' self-reports have a slightly higher correlation with psychometric scores than parental inputs.
“From Figure 4, we observed that both teens’ and parents’ ratings for mood and anxiety level are significantly correlated with psychometric scores, and teens’ self-reports have a slightly higher correlation than parental inputs.”
Quote in the paperNot a substantial claim - Findingown data
There is no significant correlation between ambient light intensity and psychometric scores.
“Conversely, there is no significant correlation between other living contexts, that is, ambient light intensity, smartphone screen usage, and psychometric scores.”
- Also stated in: § Relationship Between Smartphone Data and Psychometric Scores · plain
“However, we did not observe significant correlations between other daily living contexts (ie, ambient light intensity and smartphone screen usage) and depressive symptoms.”
Quote in the paperNot a substantial claim - Also stated in: § Relationship Between Smartphone Data and Psychometric Scores · plain
- Findingown data
Using teens' self-evaluations plus SMS, call, steps, and GPS data predicts PHQ-9 score with an RMSE of 3.23.
“By using the teens’ self-evaluations, as well as SMS, call, steps, and GPS data, we could predict the PHQ-9 score with an RMSE of 3.23.”
Quote in the paperSubstantial claim - Findingown data
Similar PHQ-9 prediction accuracy can be achieved by using only background sensor data compared with using both active self-reports and passive sensor data.
“Besides, we could achieve similar PHQ-9 prediction accuracy by using only background sensor data, compared with using both active self-reports and passive sensor data, thereby reducing the efforts of manual inputs.”
Quote in the paperSubstantial claim - Findingown data
Patients with more severe depression spend time more uniformly across different places, reflected by higher normalized entropy.
“Patients with more severe depression also visit fewer places and have lower location variance, yet they spend time more uniformly across different places, as reflected by higher normalized entropy.”
Quote in the paperNot a substantial claim - Findingown data
There is no significant correlation between smartphone screen usage and psychometric scores.
“Conversely, there is no significant correlation between other living contexts, that is, ambient light intensity, smartphone screen usage, and psychometric scores.”
- Also stated in: § Relationship Between Smartphone Data and Psychometric Scores · plain
“However, we did not observe significant correlations between other daily living contexts (ie, ambient light intensity and smartphone screen usage) and depressive symptoms.”
Quote in the paperNot a substantial claim - Also stated in: § Relationship Between Smartphone Data and Psychometric Scores · plain
- Findingown data
Parental reports about a teen's mood are correlated with the teen's baseline psychometric scores.
“Second, parental reports about a teen’s mood are correlated with the teen’s baseline psychometric scores, and thus, they provide a new dimension for tracking a teen’s mental status.”
Quote in the paperSubstantial claim - Findingown data
Using either teens' inputs or parents' inputs only achieves similar accuracy in PHQ-9 prediction, with RMSE 3.38 and 3.47, respectively.
“The results indicate that by using either teens’ inputs or parents’ inputs only, we can achieve similar accuracy in PHQ-9 prediction, with the RMSE being 3.38 and 3.47 (the range for PHQ-9 is 0-27), respectively.”
Quote in the paperSubstantial claim - Findingown data
Patients with more severe depression have lower location variance.
“Patients with more severe depression also visit fewer places and have lower location variance, yet they spend time more uniformly across different places, as reflected by higher normalized entropy.”
Quote in the paperNot a substantial claim - Findingown data
Self-evaluated mood averaged over a 2-week period significantly correlated with biweekly PHQ-9, HAM-D, and HAM-A scores (0.45≤|r|≤0.63; P=.009, .01, .003).
“We observed a significant correlation between the self-evaluated mood averaged over a 2-week period and the biweekly psychometric scores from PHQ-9, HAM-D, and HAM-A (0.45≤|r|≤0.63; P=.009, P=.01, and P=.003, respectively).”
Quote in the paperSubstantial claim - Findingown data
Adding parents' evaluations to teens' self-evaluations plus SMS, call, steps, and GPS data reduces RMSE to 2.65, the lowest error achieved.
“Furthermore, by adding the parents’ evaluations, we could reduce the RMSE to 2.65, which is also the lowest error that we could achieve.”
Quote in the paperSubstantial claim - Findingown data
Subjects with higher depression scores tend to have lower mobility, as indicated by fewer steps taken.
“From the figure, we observed that subjects with higher depression scores tend to have lower mobility, as indicated by fewer steps taken.”
Quote in the paperSubstantial claim - Findingown data
Combining self-evaluations and smartphone sensor data of the teens predicted the PHQ-9 score with an accuracy of 88% (23.77/27).
“By combining self-evaluations and smartphone sensor data of the teens, we could predict the PHQ-9 score with an accuracy of 88% (23.77/27).”
Quote in the paperSubstantial claim - Findingown data
Patients with more severe depression visit fewer places.
“Patients with more severe depression also visit fewer places and have lower location variance, yet they spend time more uniformly across different places, as reflected by higher normalized entropy.”
Quote in the paperSubstantial claim - Findingown data
The highest accuracy in predicting PHQ-9 was 90%, achieved by combining teens' and parents' inputs with SMS/call/steps/GPS data.
“The highest accuracy in predicting PHQ-9 was 90%, which was achieved by combining the teens’ and parents’ inputs with SMS/call/steps/GPS data.”
Quote in the paperSubstantial claim - Findingown data
Lower levels of mobility and fewer social interactions were predictive of higher depression symptoms.
“Lower levels of mobility and fewer social interactions were predictive of higher depression symptoms, which was consistent with a decline in mobility and social communications in individuals with depression.”
Quote in the paperSubstantial claim - Findingnone given
Submitting responses through the app took less than 15 s per day.
“Submitting the responses through the app only took less than 15 s per day.”
Quote in the paperSubstantial claim - Findingown data
The prediction accuracy could be improved by adding evaluations from parents apart from self-inputs from the teens.
“The prediction accuracy could be improved by adding the evaluations from parents apart from self-inputs from the teens.”
Quote in the paperSubstantial claim - Findingown data
Parents' inputs provide comparable performance in depression monitoring as teens' inputs.
“Moreover, parents’ inputs provide comparable performance in depression monitoring as teens’ inputs.”
Quote in the paperSubstantial claim- Rests on an assumption: Parents' evaluations of their adolescents' depressive symptoms are not biased by the parents' own mental health status.
- Findingown data
Using only passive sensor loggings achieves comparable prediction accuracy to using both sensor loggings and self-inputs.
“Our study also showed evidence that by using only passive sensor loggings, we could achieve comparable prediction accuracy by using both sensor loggings and self-inputs.”
Quote in the paperSubstantial claim - Interpretationown data
There is significant predictability of clinical measures from smartphone data.
“The major significance of the result is that there appears to be significant predictability of clinical measures from smartphone data.”
Quote in the paperSubstantial claim - Interpretationown data
Daily responses submitted through a customized smartphone app by either teens or their parents can be used as a reliable approach for monitoring depression.
“Therefore, daily responses submitted through a customized smartphone app by either teens or their parents can be used as a reliable approach for monitoring depression.”
Quote in the paperSubstantial claim - Interpretationown data
Smartphone apps such as SOLVD are a useful way to monitor depressive symptoms in clinically depressed adolescents.
“Smartphone apps such as SOLVD represent a useful way to monitor depressive symptoms in clinically depressed adolescents, and these apps correlate well with current gold-standard psychometric instruments.”
Quote in the paperSubstantial claim- Rests on an assumption: There is often no quantified information about the psychological status of an individual with depression between sporadic clinical psychometric evaluations every few months.
- Interpretationown data
Communication patterns are affected by depression severity.
“Thus, as expected, communication patterns are affected by depression severity, but our dataset did not indicate any significant patterns regarding screen usage and ambient light intensity, with the latter being a proxy for sleep duration.”
Quote in the paperSubstantial claim - Interpretationreasoning
The results are preliminary because of the small sample size.
“The results are preliminary because of the small sample size, and we plan to expand the study to a larger population.”
Quote in the paperSubstantial claim - Interpretationown data
Parental reports provide a new dimension for tracking a teen's mental status.
“Second, parental reports about a teen’s mood are correlated with the teen’s baseline psychometric scores, and thus, they provide a new dimension for tracking a teen’s mental status.”
Quote in the paperSubstantial claim - Interpretationown data
The proposed approach worked well for teenagers who are typically heavy smartphone users.
“The proposed approach worked well for teenagers who are typically heavy smartphone users.”
Quote in the paperSubstantial claim - Interpretationown data
The study expanded on prior studies by suggesting that introducing other people as human sensors could further increase the accuracy in depression monitoring.
“Our study expanded on prior studies by suggesting that introducing other people as human sensors could further increase the accuracy in depression monitoring.”
Quote in the paperSubstantial claim- Rests on an assumption: Parents' evaluations of their adolescents' depressive symptoms are not biased by the parents' own mental health status.
- Interpretationown data
The SOLVD-Teen study verified the feasibility of using smartphone user inputs and background sensor loggings to monitor adolescent patients with MDD.
“In summary, the SOLVD-Teen study verified the feasibility of using smartphone user inputs and background sensor loggings to monitor adolescent patients with MDD.”
Quote in the paperSubstantial claim
- Classified as probably not a substantial claim. 18
- May combine two claims. 18
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Findingown dataAmong the 12 parents, 5 were diagnosed with mild depression and 2 showed moderate-to-severe depression.
“In fact, 5 of the 12 parents were also diagnosed with mild depression and 2 showed moderate-to-severe depression.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Findingown dataIn the SOLVD-Teen trial, 13 adolescents diagnosed with major depressive disorder were recruited from the Adolescents in Motion (AIM) program, and one parent per adolescent was also recruited.
“For the SOLVD-Teen trial, we recruited 13 teens diagnosed with MDD from Adolescents in Motion (AIM), an intensive program for teenagers that includes multidisciplinary group therapy with the teenagers and family sessions for the parents. For every teenager recruited, one of their parents was recruited with them.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Findingown dataThe 11 analyzed adolescents had an average PHQ-9 score of 12.72 (SD 5.97), with 8 in the normal-to-mild range (PHQ-9 ≤14) and 3 in the moderate-to-severe range (PHQ-9 >14).
“The remaining 11 teens had an average PHQ-9 score of 12.72 (SD 5.97), with 8 in the normal-to-mild range (PHQ-9 ≤14) and 3 in the moderate-to-severe range (PHQ-9>14).”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Findingnone givenThe smartphone app consumes around 5 MB data and 13% battery per day.
“The smartphone app consumes around 5 MB data and 13% battery per day, which is well-acceptable for daily monitoring.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
FindingreasoningStudy recruitment ended because the AIM program closed for logistic reasons.
“Study recruitment ended as the program from which the patients were recruited shut down for logistic reasons as the AIM program closed.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingnone givenAll study participants were comfortable with the use of the technology, and neither the teens nor their parents perceived the app as invasive or burdensome.
“All the study participants were comfortable with the use of the technology, and neither the teens nor their parents perceived the app as invasive or burdensome.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
Study design and conductnone givenSubjects completed the Mini International Neuropsychiatric Interview (MINI) to confirm a diagnosis of MDD.
“Subjects also completed the Mini International Neuropsychiatric Interview (MINI) to confirm a diagnosis of MDD.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationA pioneer study used co-designed smartphone apps to monitor mood symptoms for youth with major depression, suicidal ideation, and self-harm.
“A pioneer study was conducted by co-designed smartphone apps to monitor mood symptoms for the youth with major depression, suicidal ideation, and self-harm [11].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [11] Hetrick SE Robinson J Burge E Blandon R Mobilio B Rice SM Simmons MB Alvarez-Jimenez M Goodrich S Davey CG Youth codesign of a mobile phone app to facilitate self-monitoring and management of mood symptoms in young people with major depression, suicidal ideation, and self-harm JMIR Ment Health 2018 1 23 5 1 e9 10.2196/mental.9041 29362208 29362208 PMC5801516 source
Not checked in the free run.
May combine two claims.
PremisecitationThe SOLVD-Teen study was an extension of the SOLVD pilot trial, which established a correlation between clinician-rated measures, self-rated measures, and sensor-based app measures for depressed adult patients.
“The SOLVD-Teen study was an extension of the SOLVD pilot trial [9], which established a correlation between clinician-rated measures, self-rated measures, and sensor-based app measures for depressed adult patients.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [9] Moukaddam N Truong A Cao J Shah A Sabharwal A Findings from a trial of the Smartphone and OnLine Usage-based eValuation for Depression (SOLVD) application: what do apps really tell us about patients with depression? Concordance between app-generated data and standard psychiatric questionnaires for depression and anxiety J Psychiatr Pract 2019 9 25 5 365 73 10.1097/PRA.0000000000000420 31505521 31505521 source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenThe speed thresholds for categorizing transportation modes were manually chosen.
“The speed thresholds are manually chosen, and the definition for each feature is listed below:”
Quote in the paperNot a substantial claimMay combine two claims.
Study design and conductown dataAverage compliance to submit daily evaluations through the SOLVD apps was 79.0% (498/630 days) for teens and 95.7% (603/630 days) for parents.
“The average teens’ and parents’ compliance to submit daily evaluations through the SOLVD apps was 79.0% (498/630 days) and 95.7% (603/630 days), respectively.”
- Also stated in: § Principal Findings · plain
“Both the teens and their participating parents showed a high adherence rate to submitting daily self-evaluations.”
Quote in the paperSubstantial claim- Also stated in: § Principal Findings · plain
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductown dataA total of 8 families completed the entire 8-week trial, and the others enrolled between 4 and 6 weeks.
“A total of 8 families completed the entire 8-week trial, and the others enrolled in the study between 4 and 6 weeks.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductnone givenAll patients were receiving pharmacological treatment at the time of the study, and no medication regimen changes were made through the duration.
“All patients were receiving pharmacological treatment at the time of the study, and no medication regimen changes were made through the duration.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationSeveral proof-of-concept studies have highlighted activity changes captured by smartphone sensors, and the usage patterns are associated with depressive symptoms in adult patients with MDD.
“Several proof-of-concept studies have highlighted activity changes captured by smartphone sensors, and the usage patterns are associated with depressive symptoms in adult patients with major depressive disorder (MDD) [4,5].”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [4] Kirchner T Magallón-Neri E Ortiz MS Planellas I Forns M Calderón C Adolescents' daily perception of internalizing emotional states by means of smartphone-based ecological momentary assessment Span J Psychol 2017 12 4 20 E71 10.1017/sjp.2017.70 29198232 29198232 source
Not checked in the free run.
- cites [5] Torous J Staples P Shanahan M Lin C Peck P Keshavan M Onnela J Utilizing a personal smartphone custom app to assess the Patient Health Questionnaire-9 (PHQ-9) depressive symptoms in patients with major depressive disorder JMIR Ment Health 2015 2 1 e8 10.2196/mental.3889 26543914 26543914 PMC4607379 source
Not checked in the free run.
May combine two claims.
Interpretationown dataUsing only background sensor data reduces the efforts of manual inputs while maintaining similar PHQ-9 prediction accuracy.
“Besides, we could achieve similar PHQ-9 prediction accuracy by using only background sensor data, compared with using both active self-reports and passive sensor data, thereby reducing the efforts of manual inputs.”
Quote in the paperSubstantial claimMay combine two claims.
Interpretationown dataParental data provide new useful information and make the predictors more accurate.
“Thus, parental data provide new useful information and make the predictors more accurate.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenThe clinician was blinded to the smartphone data.
“The clinician was blinded to the smartphone data.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Study design and conductnone givenThe ambient light intensity threshold τI was empirically selected.
“Then, we selected an empirical threshold τI for ambient light intensity to separate the scenario of sleeping with the light off from other scenarios.”
Quote in the paperNot a substantial claimMay combine two claims.
Study design and conductown dataOf the 13 recruited adolescents, 2 dropped out within 2 weeks and did not attend follow-up sessions, leaving 11 for analysis.
“Out of the 13 subjects, 2 were excluded from further analysis as they dropped out of the study within 2 weeks and did not attend any of the follow-up sessions.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationHigher degrees of depression are linked to reduced contact with one's social network, shorter text messages, reduced duration of calls, and less variability in geospatial activity.
“Higher degrees of depression are linked to reduced contact with one’s social network, shorter text messages, reduced duration of calls, and less variability in geospatial activity [6-9].”
Quote in the paperSubstantial claim0 of 4 sources checked- cites [6] LiKamWa R Liu Y Lane N Zhong L MoodScope: Building a Mood Sensor From Smartphone Usage Patterns Proceeding of the 11th annual international conference on Mobile systems, applications, and services 2013 MobiSys'13 June 25-28, 2013 Taipei, Taiwan 389 402 10.1145/2462456.2464449 source
Not checked in the free run.
- cites [7] Torous J Friedman R Keshavan M Smartphone ownership and interest in mobile applications to monitor symptoms of mental health conditions JMIR Mhealth Uhealth 2014 1 21 2 1 e2 10.2196/mhealth.2994 25098314 25098314 PMC4114412 source
Not checked in the free run.
- cites [8] Wahle F Kowatsch T Fleisch E Rufer M Weidt S Mobile sensing and support for people with depression: a pilot trial in the wild JMIR Mhealth Uhealth 2016 9 21 4 3 e111 10.2196/mhealth.5960 27655245 27655245 PMC5052463 source
Not checked in the free run.
- cites [9] Moukaddam N Truong A Cao J Shah A Sabharwal A Findings from a trial of the Smartphone and OnLine Usage-based eValuation for Depression (SOLVD) application: what do apps really tell us about patients with depression? Concordance between app-generated data and standard psychiatric questionnaires for depression and anxiety J Psychiatr Pract 2019 9 25 5 365 73 10.1097/PRA.0000000000000420 31505521 31505521 source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationSimilar results (higher depression associated with lower mobility) were reported in previous studies with depressed adult patients.
“Similar results were also reported in previous studies with depressed adult patients, thus further validating our observations [25].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [25] Berrouiguet S Ramírez D Barrigón ML Moreno-Muñoz P Carmona Camacho R Baca-García E Artés-Rodríguez A Combining continuous smartphone native sensors data capture and unsupervised data mining techniques for behavioral changes detection: a case series of the Evidence-Based Behavior (eB2) study JMIR Mhealth Uhealth 2018 12 10 6 12 e197 10.2196/mhealth.9472 30530465 30530465 PMC6305880
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Study design and conductnone givenThe screen on percentage threshold τS was empirically selected as 10%.
“we empirically selected 10%) during nighttime (11 PM-7 AM) and daytime (7 AM-11 PM) per day.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationChanges in daily stress levels in young adults aged 19 to 30 years were associated with changes in geospatial activity, sleep duration, and sensor-derived speech duration.
“The same observations were reproduced in younger adults (aged 19-30 years) [10], showing that changes in daily stress levels in young adults aged 19 to 30 years were associated with changes in geospatial activity, sleep duration (obtained by proxy from phone sensors), and sensor-derived speech duration.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [10] Ben-Zeev D Scherer EA Wang R Xie H Campbell AT Next-generation psychiatric assessment: using smartphone sensors to monitor behavior and mental health Psychiatr Rehabil J 2015 9 38 3 218 26 10.1037/prj0000130 25844912 25844912 PMC4564327 source
Not checked in the free run.
Classified as probably not a substantial claim.
Study design and conductown dataThe study conclusion is preliminary given the relatively small sample size of 13 families.
“The study conclusion is preliminary given the relatively small sample size of 13 families.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationNonsuicidal self-injury correlates with self-criticism and feeling criticized by others and usually links to a negative affect of brief durations.
“Similarly, nonsuicidal self-injury correlates with self-criticism and feeling criticized by others and usually links to a negative affect of brief durations [27].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [27] Allen KJ Fox KR Schatten HT Hooley JM Frequency of nonsuicidal self-injury is associated with impulsive decision-making during criticism Psychiatry Res 2019 1 271 68 75 10.1016/j.psychres.2018.11.022 30469091 30469091 PMC6382530
Not checked in the free run.
May combine two claims.
Interpretationown dataThe SOLVD-Teen app could further reduce user effort by collecting only passive data, while maintaining a comparable depression monitoring effect.
“Therefore, the SOLVD-Teen app could further reduce user effort by collecting only passive data, while maintaining a comparable depression monitoring effect.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationIn college-aged students, increased ruminations were found to relate to increasing depressive symptoms.
“In college-aged students, increased ruminations were found to relate to increasing depressive symptoms [26].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [26] Connolly SL Alloy LB Rumination interacts with life stress to predict depressive symptoms: an ecological momentary assessment study Behav Res Ther 2017 10 97 86 95 10.1016/j.brat.2017.07.006 28734979 28734979 PMC5600682
Not checked in the free run.
May combine two claims.
Interpretationown dataMobile-based measurements have potential for tracking depressive states, and introducing someone close to the subjects (eg, their parent) as a human sensor provides an additional dimension of data.
“This clearly shows the potential of mobile-based measurements for tracking depressive states and the importance of introducing someone close to the subjects (eg, their parent) as a human sensor to provide an additional dimension of data.”
Quote in the paperSubstantial claim- Rests on an assumption: Parents' evaluations of their adolescents' depressive symptoms are not biased by the parents' own mental health status.
May combine two claims.
Study design and conductnone givenThe study was limited by the relatively small sample size and the constraint of using only Android phones because of the lack of permission to access certain sensors on the iOS platform.
“The study was limited by the relatively small sample size and the constraint of using only Android phones because of the lack of permission to access certain sensors on the iOS platform.”
Quote in the paperNot a substantial claim
The paper relies on this without support.
Studies on smartphone-based depression monitoring are still limited in the context of teenagers.
“However, to date, studies on smartphone-based depression monitoring are still limited in the context of teenagers.”
The paper relies on this without support.
There is often no quantified information about the psychological status of an individual with depression between sporadic clinical psychometric evaluations every few months.
“However, a key challenge in the care of persons with depression is that there is often no quantified information about the psychological status of the individual between sporadic clinical psychometric evaluations every few months.”
The paper relies on this without support.
Parents' evaluations of their adolescents' depressive symptoms are not biased by the parents' own mental health status.
“Given the limited sample size, there is no evidence showing that parents’ evaluations were biased by their own mental health status.”
The authors state this as an assumption.
There is no a priori reason to believe that a more accurate predictor of PHQ-9 is linear.
“The reason for fitting a nonlinear regressor is that, a priori, we have no reason to believe that a more accurate predictor of PHQ-9 is linear.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Cao et al. (44) reported that in a model including HR features recorded by the fitbit, light exposure, and sleep, one of the most important features to estimate mood variation among 18 individuals with MDD was HR.
“Moreover, Cao et al. (44) reported that in a model including HR features recorded by the fitbit, light exposure, and sleep one of the most important features to estimate mood variation among 18 individuals with MDD was HR.”
From Monitoring Changes in Depression Severity Using Wearable and Mobile Sensors (2020) · cites this paper as [44]
Previous models estimating mood states with moderate accuracy have included measures of heart rate (HR) and HRV.
“Previous models estimating mood states with moderate accuracy have included measures of heart rate (HR) (27, 44) and HRV (26).”
From Monitoring Changes in Depression Severity Using Wearable and Mobile Sensors (2020) · cites this paper as [44]
Our models' RMSE values were higher than a model estimating self-report depressive symptoms among adolescents that included number of steps, activity level, number of SMS, and calls (RMSE = 2.77).
“were higher than a model estimating self-report depressive symptoms among adolescents that included number of steps, activity level, number of SMS, and calls yielded (RMSE = 2.77) (44).”
From Monitoring Changes in Depression Severity Using Wearable and Mobile Sensors (2020) · cites this paper as [44]
Cao et al. (44) documented that activity level was associated with low mood among adolescents.
“Cao et al. (44) documented that activity level was associated with low mood among adolescents.”
From Monitoring Changes in Depression Severity Using Wearable and Mobile Sensors (2020) · cites this paper as [44]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Family count conflicts with teen and parent counts
MinorThe paper reports recruiting 13 families and 13 teens, but says one family had two teens. That would place the 13 teens in at most 12 families, consistent with the reported 12 parents rather than 13 families.
“We recruited 13 families with adolescent patients diagnosed with MDD with or without comorbid anxiety disorder.”
“Their parents included 11 women and 1 man (there was 1 family with 2 teens)”
Data collection was not entirely through the app
MinorThe discussion says all study data were collected through the smartphone app, whereas the Methods explicitly include biweekly in-clinic psychometric scores.
“Besides, the data collection of the study was completely through the smartphone app; hence, no additional sensor or cost was needed.”
“We collected 3 forms of data: (1) biweekly in-clinic psychometric scores from PHQ-9, HAM-A, and HAM-D; (2) daily self-inputs submitted via the SOLVD app; (3) smartphone sensor and usage data”
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- Authors
- Cao, J., Truong, A. L., Banu, S., Shah, A. A., Sabharwal, A., & Moukaddam, N.
- Affiliations
- Rice University (US)
- Baylor College of Medicine (US)
- Corresponding author
- Moukaddam, Nidal
- Venue
- JMIR Mental Health
- Publisher
- JMIR Publications Inc.
- Published
- 2019-03-19
- Volume
- 7
- Issue
- 1
- Pages
- e14045-e14045
- ISSN
- 2368-7959 (ISSN-L)
- PMC
- PMC7007590
- OpenAlex
- W2975975413
- Cited by
- 81
- References
- 28
- Citations by year
- 2026: 12 · 2025: 20 · 2024: 18 · 2023: 10 · 2022: 13 · 2021: 5 · 2020: 2 · 2019: 1
- FWCI
- 5.93
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- JMIR Mental Health (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Rice Research Repository (Rice University) (submittedVersion, cc-by)
- Topics
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Keywords
- adolescent depression, major depressive disorder, mobile application, smartphone usage patterns, self-reported mood, parental evaluation, PHQ-9, Hamilton Depression Rating Scale, Hamilton Anxiety Rating Scale, anxiety comorbidity, usability
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