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©2019 by Ksana Health Inc. 

EVIDENCE

At Ksana Health, evidence-based practice and design are in our DNA. The key design principles that inform our products are outlined in the boxes below, as are examples of the research evidence (both from our group and others) that support these principles. 

1. Smartphones and wearables can validly measure behavior

2. These behaviors are related to mental health

3. Assessing client progress and providing feedback 

improves outcomes

4. Just-in-time nudges will improve completion of therapy homework  

5. Completing  out of session homework improves outcomes

 

1. Smartphones and wearables can validly measure behavior

1.1 Evidence that phone sensors are related to mood and mental disorders

 

  • Aledavood, T., Torous, J., Hoyos, A. M. T., Naslund, J. A., Onnela, J.-P., & Keshavan, M. (2019). Smartphone-Based Tracking of Sleep in Depression, Anxiety, and Psychotic Disorders, Current Psychiatry Reports, 1–9. 

  • Chen, Z., Lin, M., Chen, F., Lane, N., Cardone, G., Wang, R., et al. (2013). Unobtrusive Sleep Monitoring using Smartphones (pp. 1–8). Presented at the ICTs for improving Patients Rehabilitation Research Techniques, IEEE. http://doi.org/10.4108/icst.pervasivehealth.2013.252148

 

1.2 Evidence that phone sensors can measure sleep

 

  • Aledavood, T., Torous, J., Hoyos, A. M. T., Naslund, J. A., Onnela, J.-P., & Keshavan, M. (2019). Smartphone-Based Tracking of Sleep in Depression, Anxiety, and Psychotic Disorders, Current Psychiatry Reports, 1–9. http://doi.org/10.1007/s11920-019-1043-y

  • Chen, Z., Lin, M., Chen, F., Lane, N., Cardone, G., Wang, R., et al. (2013). Unobtrusive Sleep Monitoring using Smartphones (pp. 1–8). Presented at the ICTs for improving Patients Rehabilitation Research Techniques, IEEE. http://doi.org/10.4108/icst.pervasivehealth.2013.252148

1.3 Evidence the wrist heart rate monitors are accurate compared to ECG

 

  • Nelson, B. W., & Allen, N. B. (2019). Accuracy of Consumer Wearable Heart Rate Measurement During an Ecologically Valid 24-Hour Period: Intraindividual Validation Study. JMIR mHealth and uHealth, 7(3), e10828–16. http://doi.org/10.2196/10828

2. These behaviors are related to mental health

2.1 Evidence that sleep is related to mental health

 

  • Bei, B., Manber, R., Allen, N. B., Trinder, J., & Wiley, J. F. (2016). Too long, too short, or too variable? Sleep intraindividual variability and its associations with perceived sleep quality and mood in adolescents during naturalistically unconstrained sleep. Sleep, 40(2), zsw067.

  • Blake, M. J., Trinder, J. A., & Allen, N. B. (2018). Mechanisms underlying the association between insomnia, anxiety, and depression in adolescence - Implications for behavioral sleep interventions. Clinical Psychology Review, 63, 25–40. 

  • Blake, M. J., Snoep, L., Raniti, M., Schwartz, O., Waloszek, J. M., Simmons, J. G., et al. (2017). A cognitive-behavioral and mindfulness-based group sleep intervention improves behavior problems in at-risk adolescents by improving perceived sleep quality. Behaviour Research and Therapy, 99, 147–156. 

  • Littlewood, D. L., Kyle, S. D., Carter, L. A., Peters, S., Pratt, D., & Gooding, P. (2019). Short sleep duration and poor sleep quality predict next-day suicidal ideation: an ecological momentary assessment study. Psychological medicine, 49(3), 403-411.

 

2.2 Evidence that online language is related to mental health

 

  • Al-Mosaiwi, M., & Johnstone, T. (2018). In an Absolute State: Elevated Use of Absolutist Words Is a Marker Specific to Anxiety, Depression, and Suicidal Ideation. Clinical Psychological Science, 6(4), 529–542. 

  • Merchant, R. M., Asch, D. A., Crutchley, P., Ungar, L. H., Guntuku, S. C., Eichstaedt, J. C., et al. (2019). Evaluating the predictability of medical conditions from social media posts. PLoS ONE, 14(6), e0215476–12. 

  • Eichstaedt, J. C., Smith, R. J., Merchant, R. M., Ungar, L. H., Crutchley, P., Preoţiuc-Pietro, D., et al. (2018). Facebook language predicts depression in medical records. Proceedings of the National Academy of Sciences, 115(44), 11203–11208. http://doi.org/10.1073/pnas.1802331115

  • Coppersmith, G., Leary, R., Crutchley, P., & Fine, A. (2018). Natural Language Processing of Social Media as Screening for Suicide Risk. Biomedical Informatics Insights, 10, 1–11. 

2.3 Evidence that physical activity is related to mental health

 

  • Schuch, F. B., Vancampfort, D., Firth, J., Rosenbaum, S., Ward, P. B., Silva, E. S., ... & Fleck, M. P. (2018). Physical activity and incident depression: a meta-analysis of prospective cohort studies. American Journal of Psychiatry, 175(7), 631-648.

  • Stavrakakis, N., Booij, S. H., Roest, A. M., de Jonge, P., Oldehinkel, A. J., & Bos, E. H. (2015). Temporal dynamics of physical activity and affect in depressed and nondepressed individuals. Health Psychology, 34S, 1268–1277. 

  • Harvey, S. B., Hotopf, M., Overland, S., & Mykletun, A. (2010). Physical activity and common mental disorders. The British Journal of Psychiatry, 197(5), 357–364. 

 

2.4 Evidence that voice is related to mental health

 

  • Girard, J. M., & Cohn, J. F. (2015). Automated audiovisual depression analysis. Current Opinion in Psychology, 4, 75–79. 

  • Low, L.-S. A., Maddage, M. C., Lech, M., Sheeber, L. B., & Allen, N. B. (2010). Detection of Clinical Depression in Adolescents' Speech During Family Interactions. IEEE Transactions on Biomedical Engineering, 58(3), 574–586. http://doi.org/10.1109/TBME.2010.2091640

  • Ooi, K. E. B., Lech, M., & Allen, N. B. (2012). Multichannel Weighted Speech Classification System for Prediction of Major Depression in Adolescents. IEEE Transactions on Biomedical Engineering, 60(2), 497–506. 

 

2.5 Evidence that geographic movement and/or location is related to mental health

 

  • Chow PI, Fua K, Huang Y, Bonelli W, Xiong H, Barnes LE, et al. (2017). Using mobile sensing to test clinical models of depression, social anxiety, state affect, and social isolation among college students. J Med Internet Res. 19(3):e62.  https://doi.org/10.2196/jmir.6820.

  • Engemann, K., Pedersen, C. B., Arge, L., Tsirogiannis, C., Mortensen, P. B., & Svenning, J.-C. (2019). Residential green space in childhood is associated with lower risk of psychiatric disorders from adolescence into adulthood. Proceedings of the National Academy of Sciences, 116(11), 5188–5193. http://doi.org/10.1073/pnas.1807504116

 

2.6 Evidence that facial expression is related to mental health

  • Girard, J. M., Cohn, J. F., Mahoor, M. H., Mavadati, S. M., Hammal, Z., & Rosenwald, D. P. (2014). Nonverbal social withdrawal in depression: Evidence from manual and automatic analyses. Image and Vision Computing, 32(10), 641–647. http://doi.org/10.1016/j.imavis.2013.12.007

 

2.7 Evidence that music listening is related to emotional states

 

  • Park, M., Thom, J., Mennicken, S., Cramer, H., & Macy, M. (2019). Global music streaming data reveal diurnal and seasonal patterns of affective preference. Nature Human Behaviour, 1–9.

  • Schriewer K, Bulaj G. Music streaming services as adjunct therapies for depression, anxiety, and bipolar symptoms: convergence of digital technologies, mobile apps, emotions, and global mental health. Frontiers in Public Health. 2016;4:217.  https://doi.org/10.3389/fpubh.2016.00217.

 

 

3. Assessing client progress and providing feedback 

improves outcomes

  • Lambert, M. J., Whipple, J. L., Smart, D. W., Vermeersch, D. A., Nielsen, S. L., & Hawkins, E. J. (2001). The effects of providing therapists with feedback on patient progress during psychotherapy: Are outcomes enhanced?. Psychotherapy Research, 11(1), 49-68.

  • Lambert, M. J., Whipple, J. L., & Kleinstäuber, M. (2018). Collecting and delivering progress feedback: A meta-analysis of routine outcome monitoring. Psychotherapy, 55(4), 520-537

 

4. Delivering just-in-time nudges will improve completion of therapy homework  

4.1 Evidence that mobile apps can improve treatment adherence

 

  • Pérez-Jover, V., Sala-González, M., Guilabert, M., & Mira, J. J. (2019). Mobile Apps for Increasing Treatment Adherence: Systematic Review. Journal of Medical Internet Research, 21(6), e12505–14. http://doi.org/10.2196/12505

  • Tang, W., & Kreindler, D. (2017). Supporting Homework Compliance in Cognitive Behavioural Therapy: Essential Features of Mobile Apps. JMIR Mental Health, 4(2), e20–10. http://doi.org/10.2196/mental.5283

 

4.2 Evidence that just in time interventions facilitate behavior change

 

  • Klasnja, P., Smith, S., Seewald, N. J., Lee, A., Hall, K., Luers, B., et al. (2018). Efficacy of Contextually Tailored Suggestions for Physical Activity: A Micro-randomized Optimization Trial of HeartSteps. Annals of Behavioral Medicine, 53(6), 573–582. http://doi.org/10.1093/abm/kay067

  • Rathbone, A. L., & Prescott, J. (2017). The Use of Mobile Apps and SMS Messaging as Physical and Mental Health Interventions: Systematic Review. Journal of Medical Internet Research, 19(8), e295–13. http://doi.org/10.2196/jmir.7740

 

5. Completing  out of session homework improves outcomes

 

  • Kazantzis, N., Whittington, C., Zelencich, L., Kyrios, M., Norton, P. J., & Hofmann, S. G. (2016). Quantity and quality of homework compliance: a meta-analysis of relations with outcome in cognitive behavior therapy. Behavior Therapy, 47(5), 755-772.  http://doi.org/10.1016/j.beth.2016.05.002