Our Commitment to Scientific Excellence
At Ksana Health, rigorous science and evidence-based design are foundational to our mission. Our co-founder and CEO, Dr. Nick Allen, is a leading clinician-scientist with a significant track record of funded research and peer-reviewed publications (view profile). Alongside our experienced clinical science team, he understands the unique requirements of researchers seeking funding and implementing innovative studies. We provide state-of-the-art mobile sensing technologies tailored specifically for cutting-edge research.
Proven Track Record in Research and Innovation
Ksana Health’s digital health platforms, EARS and Vira, are trusted by over 50 prestigious universities and healthcare organizations worldwide. Our technologies have been central to successful research endeavors, securing funding for over 15 major grant applications from institutions including the NIH.
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
De Angel, V., Lewis, S., White, K., Oetzmann, C., Leightley, D., Oprea, E., … & Hotopf, M. (2022). Digital health tools for the passive monitoring of depression: a systematic review of methods. NPJ digital medicine, 5(1), 3.
Webb, C. A., Ren, B., Rahimi-Eichi, H., Gillis, B. W., Chung, Y., & Baker, J. T. (2025). Personalized prediction of negative affect in individuals with serious mental illness followed using long-term multimodal mobile phenotyping. Translational Psychiatry, 15(1), 1-9.
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, 21(7), 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.
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.
2. These behaviors are related to mental health
2.1 Evidence that patterns of mobile device use are related to mental health
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.
Liang, P. P., Liu, T., Cai, A., Muszynski, M., Ishii, R., Allen, N., Auerbach, R., Brent, D., Salakhutdinov, R., & Morency, L.-P. (2021). Learning language and multimodal privacy-preserving markers of mood from Mobile Data. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers).
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., Murray, G., Blake, L., Landau, E. R., Dahl, R. E., Bootzin, R., McMakin, D. L., Dudgeon, P., Trinder, J., & Allen, N. B. (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.
Funkhouser et al. (2024). Detecting adolescent depression through passive monitoring of linguistic markers in smartphone communication. Journal of Child Psychology and Psychiatry, 65(7), 932-941.Li et al. (2023). Capturing mood dynamics through adolescent smartphone social communication. Journal of Psychopathology and Clinical Science, 132(8), 1072.
Li et al. (2023). Capturing mood dynamics through adolescent smartphone social communication. Journal of Psychopathology and Clinical Science, 132(8), 1072.
Liu, T., Meyerhoff, J., Eichstaedt, J. C., Karr, C. J., Kaiser, S. M., Kording, K. P., Mohr, D. C., & Ungar, L. H. (2022). The relationship between text message sentiment and self-reported depression. Journal of Affective Disorders, 302, 7–14.
McNeilly et al. (2023). Adolescent social communication through smartphones: Linguistic features of internalizing symptoms and daily mood. Clinical Psychological Science, 11(6), 1090-1107.
McNeilly et al. (2024). Neural correlates of depression-related smartphone language use in adolescents. NPP—Digital Psychiatry and Neuroscience, 2(1), 11.
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(1), 4525.
2.4 Evidence that physical activity is related to mental health
Harvey, S., Hotopf, M., Øverland, S., & Mykletun, A. (2010). Physical activity and common mental disorders. British Journal of Psychiatry, 197(5), 357-364.
Noetel, M. et al. Effect of exercise for depression: systematic review and network meta-analysis of randomised controlled trials. BMJ 384, e075847 (2024).
Schuch, F. B., Vancampfort, D., Firth, J., Rosenbaum, S., Ward, P. B., Silva, E. S., Hallgren, M., Ponce De Leon, A., Dunn, A. L., Deslandes, A. C., Fleck, M. P., Carvalho, A. F., & Stubbs, B. (2018). (2018). Physical activity and incident depression: a meta-analysis of prospective cohort studies. American Journal of Psychiatry, 175(7), 631-648.
Singh, B. et al. Effectiveness of physical activity interventions for improving depression, anxiety and distress: an overview of systematic reviews. Br. J. Sports Med. 57, 1203–1209 (2023).
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, 34(Suppl), 1268–1277.
2.5 Evidence that geographic movement and/or location is related to mental health
Auerbach et al. (2025). Using Smartphone GPS Data to Detect the Risk of Adolescent Suicidal Thoughts and Behaviors. JAMA Network Open, 8(1), e2456429
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.
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.
Stamatis, C. A., Meyerhoff, J., Meng, Y., Lin, Z. C. C., Cho, Y. M., Liu, T., … & Mohr, D. C. (2024). Differential temporal utility of passively sensed smartphone features for depression and anxiety symptom prediction: a longitudinal cohort study. npj Mental Health Research, 3(1), 1.
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, 3(3), 230–236.
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.
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.
Rognstad, K., Wentzel-Larsen, T., Neumer, S. P., & Kjøbli, J. (2023). A systematic review and meta-analysis of measurement feedback systems in treatment for common mental health disorders. Administration and Policy in Mental Health and Mental Health Services Research, 50(2), 269-282.
4. Delivering just-in-time interventions can improve treatment outcomes
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.
Tang, W., & Kreindler, D. (2017). Supporting Homework Compliance in Cognitive Behavioural Therapy: Essential Features of Mobile Apps. JMIR Mental Health, 4(2), e20–10.
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.
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.
4.3 Evidence that completing between session therapy tasks 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.