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.

Smartphones and wearables can validly measure behavior

These behaviors are related to mental health

Assessing client progress and providing feedback improves outcomes

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

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 patterns of mobile app use are related to mental health

Escobar-Viera, C. G., Shensa, A., Bowman, N. D., Sidani, J. E., Knight, J., James, A. E., & Primack, B. A. (2018). Passive and active social media use and depressive symptoms among United States adults. Cyberpsychology, Behavior, and Social Networking, 21(7), 437-443.

Kim, S., Favotto, L., Halladay, J., Wang, L., Boyle, M. H., & Georgiades, K. (2020). Differential associations between passive and active forms of screen time and adolescent mood and anxiety disorders. Social Psychiatry and Psychiatric Epidemiology, 55(11), 1469–1478.

Liu, T., Liang, P. P., Muszynski, M., Ishii, R., Brent, D., Auerbach, R., Allen, N.B., & Morency, L. P. (2020). Multimodal privacy-preserving mood prediction from mobile data: A preliminary study. arXiv preprint arXiv:2012.02359.

Primack, B. A., Shensa, A., Escobar-Viera, C. G., Barrett, E. L., Sidani, J. E., Colditz, J. B., & James, A. E. (2017). Use of multiple social media platforms and symptoms of depression and anxiety: A nationally-representative study among US young adults. Computers in human behavior, 69, 1-9.

Thorisdottir, I. E., Sigurvinsdottir, R., Asgeirsdottir, B. B., Allegrante, J. P., & Sigfusdottir, I. D. (2019). Active and passive social media use and symptoms of anxiety and depressed mood among Icelandic adolescents. Cyberpsychology, Behavior, and Social Networking, 22(8), 535-542.

2.2 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.

2.3 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.

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.

Edwards, T., & Holtzman, N. S. (2017). A meta-analysis of correlations between depression and first person singular pronoun use. Journal of Research in Personality, 68, 63–68.

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

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.

Vine, V., Boyd, R. L., & Pennebaker, J. W. (2020). Natural emotion vocabularies as windows on distress and well-being. Nature Communications, 11, 4525.

2.4 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.

2.5 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.

Rohani, D. A., Faurholt-Jepsen, M., Kessing, L. V., & Bardram, J. E. (2018). Correlations between objective behavioral features collected from mobile and wearable devices and depressive mood symptoms in patients with affective disorders: systematic review. JMIR mHealth and uHealth, 6(8), e165

Saeb, S., Lattie, E. G., Kording, K. P., & Mohr, D. C. (2017). Mobile phone detection of semantic location and its relationship to depression and anxiety. JMIR mHealth and uHealth, 5(8), e112.

2.6 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

3.1 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

5.1 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