Objectives Despite the proliferation of mobile mental health apps, evidence of their efficacy around anxiety or depression is inadequate as most studies lack appropriate control groups. Given that apps are designed to be scalable and reusable tools, insights concerning their efficacy can also be assessed uniquely through comparing different implementations of the same app. This exploratory analysis investigates the potential to report a preliminary effect size of an open-source smartphone mental health app, mindLAMP, on the reduction of anxiety and depression symptoms by comparing a control implementation of the app focused on self-assessment to an intervention implementation of the same app focused on CBT skills. Methods A total of 328 participants were eligible and completed the study under the control implementation and 156 completed the study under the intervention implementation of the mindLAMP app. Both use cases offered access to the same in-app self-assessments and therapeutic interventions. Multiple imputations were utilized to impute the missing Generalized Anxiety Disorder-7 and Patient Health Questionnaire-9 survey scores of the control implementation. Results Post hoc analysis revealed small effect sizes of Hedge's g = 0.34 for Generalized Anxiety Disorder-7 and Hedge's g = 0.21 for Patient Health Questionnaire-9 between the two groups. Conclusions mindLAMP shows promising results in improving anxiety and depression outcomes in participants. Though our results mirror the current literature in assessing mental health apps’ efficacy, they remain preliminary and will be used to inform a larger, well-powered study to further elucidate the efficacy of mindLAMP.

More from our Archive

• DOI: 10.3390/healthcare11131946 2023

  Antimicrobial Resistance: A Growing Serious Threat for Global Public Health

  Md. Abdus Salam, Md. Yusuf Al-Amin, Moushumi Tabassoom Salam, Jogendra Singh Pawar, Naseem Akhter, Ali A. Rabaan, Mohammed A. A. Alqumber

• DOI: 10.1177/20552076231186520 2023

  Artificial intelligence in healthcare: Complementing, not replacing, doctors and healthcare providers

  Emre Sezgin

• DOI: 10.1177/20552076231186064 2023

  Medical artificial intelligence ethics: A systematic review of empirical studies

  Lu Tang, Jinxu Li, Sophia Fantus

• DOI: 10.3390/healthcare11142016 2023

  Self-Compassion during COVID-19 in Non-WEIRD Countries: A Narrative Review

  Yasuhiro Kotera, Ann Kirkman, Julie Beaumont, Magdalena A. Komorowska, Elizabeth Such, Yudai Kaneda, Annabel Rushforth

• DOI: 10.3390/healthcare11142047 2023

  The Effect of Cardiopulmonary Resuscitation (CPR) Education on the CPR Knowledge, Attitudes, Self-Efficacy, and Confidence in Performing CPR among Elementary School Students in Korea

  Jang-Sik Ko, Seon-Rye Kim, Byung-Jun Cho

• DOI: 10.3390/healthcare11142046 2023

  ChatGPT Knowledge Evaluation in Basic and Clinical Medical Sciences: Multiple Choice Question Examination-Based Performance

  Sultan Ayoub Meo, Abeer A. Al-Masri, Metib Alotaibi, Muhammad Zain Sultan Meo, Muhammad Omair Sultan Meo

• DOI: 10.1177/20552076231186069 2023

  Perceptions of a machine learning-based lower-limb exercise training system among older adults with knee pain

  Tianrong Chen, Calvin Kalun Or

• DOI: 10.3390/healthcare11131921 2023

  Lessons for Medical and Health Education Learned from the COVID-19 Pandemic

  Zhanna Gardanova, Olga Belaia, Svetlana Zuevskaya, Klavdiya Turkadze, Wadim Strielkowski

• DOI: 10.3390/healthcare11131896 2023

  Anomaly Detection in Endemic Disease Surveillance Data Using Machine Learning Techniques

  Peter U. Eze, Nicholas Geard, Ivo Mueller, Iadine Chades

• DOI: 10.3390/healthcare11142029 2023

  The Prevalence and Associated Factors of Academic Stress among Medical Students of King Khalid University: An Analytical Cross-Sectional Study

  Maram M. Al-Shahrani, Bushra S. Alasmri, Reham M. Al-Shahrani, Najwa M. Al-Moalwi, Amar A. Al Qahtani, Aesha F. Siddiqui