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Cited by 333Digital Mental Health InterventionsMobile Health and mHealth ApplicationsMental Health Research Topics

Mobile Sensing and Support for People With Depression: A Pilot Trial in the Wild

Open access
Wahle, Fabian, Kowatsch, Tobias, Fleisch, Elgar, Rufer, Michael, Weidt, Steffi·2016·JMIR mHealth and uHealth
DOI 10.2196/mhealth.5960PMC PMC5052463
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Abstract

BACKGROUND: Depression is a burdensome, recurring mental health disorder with high prevalence. Even in developed countries, patients have to wait for several months to receive treatment. In many parts of the world there is only one mental health professional for over 200 people. Smartphones are ubiquitous and have a large complement of sensors that can potentially be useful in monitoring behavioral patterns that might be indicative of depressive symptoms and providing context-sensitive intervention support. OBJECTIVE: The objective of this study is 2-fold, first to explore the detection of daily-life behavior based on sensor information to identify subjects with a clinically meaningful depression level, second to explore the potential of context sensitive intervention delivery to provide in-situ support for people with depressive symptoms. METHODS: A total of 126 adults (age 20-57) were recruited to use the smartphone app Mobile Sensing and Support (MOSS), collecting context-sensitive sensor information and providing just-in-time interventions derived from cognitive behavior therapy. Real-time learning-systems were deployed to adapt to each subject's preferences to optimize recommendations with respect to time, location, and personal preference. Biweekly, participants were asked to complete a self-reported depression survey (PHQ-9) to track symptom progression. Wilcoxon tests were conducted to compare scores before and after intervention. Correlation analysis was used to test the relationship between adherence and change in PHQ-9. One hundred twenty features were constructed based on smartphone usage and sensors including accelerometer, Wifi, and global positioning systems (GPS). Machine-learning models used these features to infer behavior and context for PHQ-9 level prediction and tailored intervention delivery. RESULTS: A total of 36 subjects used MOSS for ≥2 weeks. For subjects with clinical depression (PHQ-9≥11) at baseline and adherence ≥8 weeks (n=12), a significant drop in PHQ-9 was observed (P=.01). This group showed a negative trend between adherence and change in PHQ-9 scores (rho=-.498, P=.099). Binary classification performance for biweekly PHQ-9 samples (n=143), with a cutoff of PHQ-9≥11, based on Random Forest and Support Vector Machine leave-one-out cross validation resulted in 60.1% and 59.1% accuracy, respectively. CONCLUSIONS: Proxies for social and physical behavior derived from smartphone sensor data was successfully deployed to deliver context-sensitive and personalized interventions to people with depressive symptoms. Subjects who used the app for an extended period of time showed significant reduction in self-reported symptom severity. Nonlinear classification models trained on features extracted from smartphone sensor data including Wifi, accelerometer, GPS, and phone use, demonstrated a proof of concept for the detection of depression superior to random classification. While findings of effectiveness must be reproduced in a RCT to proof causation, they pave the way for a new generation of digital health interventions leveraging smartphone sensors to provide context sensitive information for in-situ support and unobtrusive monitoring of critical mental health states.

AI snapshot

Written from the paper’s full text.

TL;DR

This pilot trial deployed the MOSS smartphone app to provide context-sensitive CBT-based interventions and passively infer depression level from sensor data in adults with depressive symptoms. Among clinically depressed participants who used the app for ≥8 weeks, PHQ-9 scores dropped significantly, and machine-learning models distinguished PHQ-9≥11 from PHQ-9≤10 with modest accuracy.

Key findings

  • Of 126 recruited adults, 36 used MOSS for ≥2 weeks; among those with baseline PHQ-9≥11 and adherence ≥8 weeks (n=12), median PHQ-9 fell from 13.00 at baseline to 10.00 at t8, with a significant Wilcoxon signed-rank result (P=.01).
  • In that same clinically depressed group, adherence was negatively correlated with change in PHQ-9 (rho=−.498, P=.099).
  • Binary classification of 143 biweekly PHQ-9 samples using a PHQ-9≥11 cutoff achieved 60.1% accuracy for Random Forest and 59.1% for SVM in the abstract; Table 4 reports 61.5% accuracy for Random Forest (ntrees=450) and 59.4% for SVM, with SVM sensitivity 72.5 and specificity 47.3, and Random Forest sensitivity 62.3 and specificity 60.8.
  • Participants with PHQ-9<11 at baseline and ≥4 weeks of use (n=8) did not show a significant difference between t0 and t4 (P=.22).

Methods

  • Monocentric, single-arm clinical pilot study conducted in Switzerland from January to September 2015; 126 adults aged 20–57 were recruited from the general public, with rolling enrollment and electronic informed consent.
  • Participants used the MOSS app, which collected smartphone sensor and usage data (accelerometer, WiFi, GPS, phone use, calls, texts, calendar events) and delivered CBT-derived interventions; PHQ-9 was collected biweekly.
  • Analyses included Wilcoxon signed-rank tests for PHQ-9 change, Spearman correlation between MOSS sessions and PHQ-9 change, and supervised machine learning using 120 features with SVM and Random Forest under leave-one-out cross-validation for PHQ-9≥11 vs ≤10 classification.

Limitations

  • The paper states the study was nonrandomized, uncontrolled, and single-arm, so it cannot prove a causal link between symptom improvement and MOSS use; it also did not collect control variables such as other current treatments.
  • The paper notes PHQ-9 is strongly correlated with depression but not all elevated PHQ-9 subjects are certain to have depression, and the pilot did not quantify the efficacy of the recommendation algorithm.
  • Evident from the text: dropout was high (64/126, 50.8%, uninstalled within 2 weeks; only 36 used MOSS ≥2 weeks), the clinical symptom-change analysis included only 12 subjects, and classification accuracy was modest.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.2196/mhealth.5960

Cites

Statistics

Nothing to recompute.

Claims the paper makes

0 of 27 cited sources checked · 0 supported · 0 flagged

  • Findingown data

    In the group with clinical depression at baseline and adherence ≥8 weeks, there was a negative trend between adherence and change in PHQ-9 scores (rho=−.498, P=.099).

    “This group showed a negative trend between adherence and change in PHQ-9 scores (rho=−.498, P=.099).”
    Quote in the paperNot a substantial claim
  • Findingown data

    Binary classification of biweekly PHQ-9 samples (n=143) with a cutoff of PHQ-9≥11 using Random Forest and Support Vector Machine leave-one-out cross validation achieved 60.1% and 59.1% accuracy, respectively.

    “Binary classification performance for biweekly PHQ-9 samples (n=143), with a cutoff of PHQ-9≥11, based on Random Forest and Support Vector Machine leave-one-out cross validation resulted in 60.1% and 59.1% accuracy, respectively.”
    Quote in the paperNot a substantial claim
  • Findingown data

    Among subjects with clinical depression (PHQ-9≥11) at baseline and adherence ≥8 weeks (n=12), PHQ-9 scores dropped significantly from before to after intervention (P=.01).

    “For subjects with clinical depression (PHQ-9≥11) at baseline and adherence ≥8 weeks (n=12), a significant drop in PHQ-9 was observed (P=.01).”
    • Also stated in: § Conclusions · plain
      “Subjects who used the app for an extended period of time showed significant reduction in self-reported symptom severity.”
    Quote in the paperSubstantial claim
  • Findingown data

    A total of 126 subjects were recruited from the general public.

    “A total of 126 subjects were recruited from the general public.”
    Quote in the paperNot a substantial claim
  • Findingown data

    A Kolmogorov-Smirnov test rejected the normality assumption of PHQ-9 scores.

    “A Kolmogorov-Smirnov test [42] rejected the normality assumption”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [42] source

      Not checked in the free run.

  • The quote only partly states this claim. 2
  • Classified as probably not a substantial claim. 20
  • May combine two claims. 9

  • The quote only partly states this claim.

    Findingown data

    There is a negative correlation between cumulated change in PHQ-9 and total number of MOSS app uses between t0 and tend, with rho=-.498 and P=.099 for the 12 clinically depressed subjects with adherence ≥4 weeks.

    “The scatter plots indicates a negative correlation between cumulated change in PHQ-9 and the total number of MOSS app uses between t0 and tend.”
    • Also stated in: § Relationship of MOSS Usage and Symptom Reduction · plain
      “We observed a negative correlation with rho=-.498 and P=.099.”
    • Also stated in: § Principal Findings · hedged
      “Among these participants, the relation between frequency of MOSS app usage and change in PHQ-9 scores showed a negative trend.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    Figure 9 shows the sample distribution of 143 PHQ-9 samples of 36 subjects with adherence of at least 2 weeks.

    “Figure 9 shows the sample distribution of the 143 PHQ-9 samples of the 36 subjects with an adherence of at least 2 weeks collected during the trial.”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim.

    Findingown data

    Spearman correlation was used because both distributions deviated from normality (P<.001, Kolmogorov-Smirnov test).

    “Spearman correlation was used because both distributions deviated from normality (P<.001, Kolmogorov-Smirnov test).”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingreasoning

    The majority of participants came from Switzerland and Germany.

    “Because the study was primarily advertised in Switzerland and on German-speaking Internet forums, the majority of participants came from Switzerland and Germany.”
    Quote in the paperNot a substantial claim

  • The quote only partly states this claim.

    Findingown data

    For participants with a clinically relevant PHQ-9 score and extended MOSS app adherence, a significant drop in PHQ-9 was observed at t=6 and t=8 compared to t0.

    “At t=6 and t=8 we observe a significant difference in means.”
    • Also stated in: § Principal Findings · plain
      “For participants with a clinically relevant PHQ-9 score and an extended MOSS app adherence, a significant drop in PHQ-9 was observed.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Cognitive-behavioral therapy (CBT) has been proven to be effective for depression.

    “Traditionally, depression is treated with medication and/or face to face psychotherapy using methods such as cognitive-behavioral therapy (CBT), which has been proven to be effective [4].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [4] source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    Many digital versions of CBT presented reasonable effect sizes, sometimes even on a par with face to face therapy.

    “Many of these solutions presented reasonable effects sizes [6], sometimes even on a par with face to face therapy [7].”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [6] source

      Not checked in the free run.

    • cites [7] source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Duration and time of the day of stays at different physical locations are related to a person's mental state and can be approximated by WiFi and global positioning systems (GPS) information.

    “duration, and time of the day of stays at different physical locations were shown to be related to a person’s mental state and can be approximated by WiFi and global positioning systems (GPS) information [17,18].”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [17] source

      Not checked in the free run.

    • cites [18] source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    Depression is associated with worse global outcomes for the affected individual, including reduced social functioning, lower quality of life in regards to health, inability to return to work, as well as suicide.

    “Beyond its burden on society, depression is associated with worse global outcomes for the affected individual, including reduced social functioning, lower quality of life in regards to health, inability to return to work, as well as suicide [3].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [3] Holden, C, 2000, Science source

      Not checked in the free run.

  • Classified as probably not a substantial claim.

    Study design and conductcitation

    Leave-one-out cross validation was conducted for unbiased performance estimation of both classifiers.

    “For unbiased performance estimation of both classifiers, leave-one-out cross validation was conducted [51].”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [51] Kearns, M, 1999, Neural Comput source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Social activity is highly related to a subject's mental state and the risk of developing depression.

    “It is highly related to a subject’s mental state and the risk of developing depression [19,20].”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [19] George, LK, 1989, Br J Psychiatry source

      Not checked in the free run.

    • cites [20] source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductnone given

    The study was conducted in full accordance with the Declaration of Helsinki.

    “It was conducted in full accordance with the Declaration of Helsinki”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductreasoning

    Not all subjects with an elevated PHQ-9 are certain to have a depression.

    “not all subjects with an elevated PHQ-9 are certain to have a depression.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: The PHQ-9 is strongly correlated with depression.

  • Classified as probably not a substantial claim.

    Study design and conductcitation

    The average of leave-one-out test scores is reported as the unbiased performance estimate.

    “The average of these scores is reported as the unbiased performance estimate [52].”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [52] KohaviRA study of cross-validation and bootstrap for accuracy estimation and model selectionProceedings of the 14th international joint conference on Artificial intelligence1995International joint conference on Artificial intelligenceAugust 20, 1995Montreal, CannadaSan FranciscoKaufmann Publishers Inc

      Not checked in the free run.

  • Classified as probably not a substantial claim.

    Premisecitation

    The standard deviation of the 3D acceleration norm resembles intensity of physical activity of 2 widely used commercial acceleration-based activity trackers with reasonable consensus.

    “A recent study showed that the standard deviation of the 3D acceleration norm resembles intensity of physical activity of 2 widely used commercial acceleration-based activity trackers with reasonable consensus [29].”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [29] source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductnone given

    Two clinically trained psychologists predefined rules to prevent the MOSS app making unreasonable intervention recommendations.

    “In addition, 2 clinically trained psychologists predefined rules to prevent the MOSS app making unreasonable intervention recommendations.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    CBT is often regarded as the mental health intervention of choice due to its large evidence on a variety of psychological disorders.

    “CBT is often regarded as the mental health intervention of choice due to its large evidence on a variety of psychological disorders [4].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [4] source

      Not checked in the free run.

  • Classified as probably not a substantial claim.

    Study design and conductown data

    All subjects provided electronic informed consent prior to participation.

    “with all subjects providing their electronic informed consent prior to participation.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Study design and conductcitation

    The walking detection approach is robust to variance in the orientation of the smartphone and different walking speeds.

    “We found, that this approach is robust to variance in the orientation of the smartphone was confirmed by Kunze and Lukowicz [30] and different walking speeds.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [30] source

      Not checked in the free run.

  • May combine two claims.

    Study design and conductreasoning

    The clinical study is based on a nonrandomized, uncontrolled single-arm study design, which rules out the possibility to prove a direct causal link between symptom improvement and MOSS app use.

    “Even though causation cannot be tested with the study design”
    • Also stated in: § Limitations · plain
      “The clinical study carried out is based on a nonrandomized, uncontrolled single-arm study design, which rules out the possibility to prove a direct causal link between symptom improvement and MOSS app use.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Premisecitation

    Saeb et al used GPS sensors and phone use statistics to distinguish people without (PHQ-9<5) from people with signs of depression (PHQ-9≥5) in a lab experiment over 2 weeks with high accuracy.

    “Saeb et al [17] used the information of GPS sensors and phone use statistics to distinguish people without (Patient Health Questionnaire, PHQ-9, <5) from people with signs of depression (PHQ-9≥5) in a lab experiment over 2 weeks with high accuracy.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [17] source

      Not checked in the free run.

  • Classified as probably not a substantial claim.

    Premisecitation

    A PHQ-9 cut off value of 11 separates people with (≥11) from people without (≤10) a clinically relevant depression level.

    “we chose a PHQ-9 cut off value of 11, in line with the PHQ-9 [22] to separate people with (≥11) from people without (≤10) a clinically relevant depression level.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [22] source

      Not checked in the free run.

  • Classified as probably not a substantial claim.

    Study design and conductreasoning

    The 2-minute time window for computing standard deviation of 3D acceleration norm was appropriate for the app's needs.

    “For this trial, we used a time window of 2 minutes. As we did not aim at classifying micro movement, this window size was appropriate for our app needs and trades of phone memory usage and frequency of computation and information gain.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim.

    Premisecitation

    CBT is a highly-structured psychological treatment.

    “CBT is a highly-structured psychological treatment [35].”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [35] source

      Not checked in the free run.

  • Classified as probably not a substantial claim. May combine two claims.

    Interpretationreasoning

    After 2 weeks, the MOSS app adapts to the subject's actual behavior and does not suffer from potentially flawed assumptions about a subject's behavior with respect to the general population.

    “This way, the MOSS app does not suffer from potentially flawed assumptions about a subject’s behavior with respect to the general population and adapts to the subject’s actual behavior.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Study design and conductnone given

    The study was approved by the local ethics committee of the Canton of Zurich and the Swiss Agency for Therapeutic Products.

    “The study was approved by the local ethics committee of the Canton of Zurich in Switzerland and the Swiss Agency for Therapeutic Products.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S4 (based).

    Interpretationnone given

    To our best knowledge, this study presents the first trial of a context sensitive smartphone app to support people with depressive symptoms under real life conditions.

    “To our best knowledge, this study presents the first trial of a context sensitive smartphone app to support people with depressive symptoms under real life conditions.”
    Quote in the paperNot a substantial claim
27 of 27

  • The authors state this as an assumption.

    People with depression have characteristic daily behaviors (e.g., time spent at home, number of calls, walking time) that can be used to tailor interventions.

    “In the first phase it delivered interventions based on assumptions about the behavior of the general depressed population and handcrafted weights for appropriate interventions to be delivered depending on the characteristics of the context”
    “In Table 1, example assumptions about the general depressed population and the characteristics of a subset of context features are presented.”

  • The authors state this as an assumption.

    Past ratings of an intervention represent the preference for an intervention and therefore should have the highest impact on the scoring function.

    “The values follow the assumption that past ratings of an intervention represent the preference for an intervention and therefore should have the highest impact on the scoring function.”

  • The authors state this as an assumption.

    The decision to cancel an intervention early is not necessarily related to a subject's general liking of the intervention.

    “The decision to cancel an intervention early, is not necessarily related to a subject’s general liking of the intervention, therefore the impact is significantly lower than past ratings.”

  • The paper relies on this without support.

    Smartphones offer numerous sources of information acting as proxies for social activities such as the frequency and average duration of calls, or the number of different persons being contacted.

    “Smartphones offer numerous sources of information acting as proxies for social activities such as the frequency and average duration of calls, or the number of different persons being contacted.”

  • The authors state this as an assumption.

    CBT is based on the assumption that thoughts determine how one feels, behaves, and physically reacts.

    “It is based on the assumption that thoughts determine how one feels, behaves, and physically reacts.”

  • The authors state this as an assumption.

    Changing maladaptive thinking leads to change in affect and behavior.

    “This form of intervention contains various treatments using cognitive and behavioral techniques with the assumption that changing maladaptive thinking leads to change in affect and behavior.”

  • The authors state this as an assumption.

    The system compares behavior proxies to assumptions about healthy behavior.

    “in comparison to assumptions about healthy behavior”

  • The paper relies on this without support.

    The PHQ-9 is strongly correlated with depression.

    “although research has shown that the PHQ-9 is strongly correlated with depression”
8 of 8

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?

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.

  • Classification accuracies differ between the abstract and results

    Major

    The abstract reports 60.1% accuracy for Random Forest and 59.1% for Support Vector Machine, whereas the results report 61.5 and 59.4, respectively.

  • Random Forest evaluation method is reported inconsistently

    Minor

    The methods say leave-one-out cross-validation scores were reported for both classifiers, but the results describe the reported Random Forest performance as out-of-bag performance.

2 of 2

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References 44 · Cited by 333

Research works printed but not linked: 6

  1. WahleFKowatschTTowards the design of evidence-based mental health information systems: a preliminary literature review2014Proceedings of the 35th International Conference on Information SystemsDec 12-14, 2014Auckland, NZ
  2. Kotsiantis, SB, 2007, Emerging Artificial Intelligence Applications in Computer Engineering: Real Word AI Systems with Applications in eHealth, HCI, Information Retrieval ... in Artificial Intelligence and Applications)
  3. 10.1016/j.jad.2013.01.035 · DOI · Paper page
  4. RekimotoJMiyakiTIshizawaTWiFi-based continuous location logging for life pattern analysisLecture Notes in Computer Science2007The 3rd International symposium of Location and Context AwarenessSeptember 20-21, 2007Oberpfaafenhofen, GermanyBerlin HeidelbergSpringer3549
  5. LovelockHMatthewsRMurphyKAustralian Psychological Association2016-05-09Evidence Based Psychological Interventions in the Treatment of Mental Disorders: A Literature Review https://www.psychology.org.au/Assets/Files/Evidence-Based-Psychological-Interventions.pdf
  6. KohaviRA study of cross-validation and bootstrap for accuracy estimation and model selectionProceedings of the 14th international joint conference on Artificial intelligence1995International joint conference on Artificial intelligenceAugust 20, 1995Montreal, CannadaSan FranciscoKaufmann Publishers Inc

Authors
Wahle, F., Kowatsch, T., Fleisch, E., Rufer, M., & Weidt, S.
Affiliations
  • ETH Zurich (CH)
  • University of St.Gallen (CH)
  • University of Zurich (CH)
Corresponding author
Kowatsch, Tobias
ORCID
Venue
JMIR mHealth and uHealth
Publisher
JMIR Publications Inc.
Published
2016-09-21
Volume
4
Issue
3
Pages
e111
ISSN
2291-5222 (ISSN-L)
DOI
10.2196/mhealth.5960
PMC
PMC5052463
OpenAlex
W2414738784
Cited by
333
References
44
Citations by year
2026: 12 · 2025: 19 · 2024: 36 · 2023: 70 · 2022: 57 · 2021: 46 · 2020: 29 · 2019: 29 · 2018: 19 · 2017: 14 · 2016: 2
FWCI
32.87
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • JMIR mhealth and uhealth (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Zurich Open Repository and Archive (University of Zurich) (acceptedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Repository for Publications and Research Data (ETH Zurich) (submittedVersion, cc-by)
  • Zurich Open Repository and Archive (University of Zurich) (submittedVersion, cc-by)
  • ETH Zürich Research Collection (cc-by)
  • Zurich Open Repository and Archive (University of Zurich)
Topics
  • Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
  • Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
Keywords
depression, smartphone sensors, mobile sensing, context-aware interventions, cognitive behavioral therapy, PHQ-9, depression detection, GPS, accelerometer, Wi-Fi, personalized interventions, medication adherence

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