Feasibility of Popular m-Health Technologies for Activity Tracking Among Individuals with Serious Mental Illness
Open accessAbstract
Obesity prevalence is nearly double among individuals with serious mental illness (SMI), including schizophrenia spectrum disorders, bipolar disorder, or major depressive disorder, compared with the general population. Emerging mobile health (m-health) technologies are increasingly available and offer the potential to support lifestyle interventions targeting weight loss, yet the practical feasibility of using these technologies in this high-risk group has not been established. We evaluated the feasibility and acceptability of popular m-health technologies for activity tracking among overweight and obese individuals with SMI. We provided wearable activity monitoring devices (FitBit [San Francisco, CA] Zip™ or Nike Inc. [Beaverton, OR] FuelBand) and smartphones (Apple [Cupertino, CA] iPhone ® 4S) for accessing the smartphone application for each device to participants with SMI enrolled in a weight loss program. Feasibility of these devices was measured by the frequency of use over time. Acceptability was measured through qualitative follow-up interviews with participants. Ten participants with SMI wore the devices for a mean of 89% (standard deviation=13%) of the days in the study. Five participants wore the devices 100% of the time. Participants reported high satisfaction, stating the devices were easy to use, helpful for setting goals, motivational, and useful for self-monitoring. Several participants liked the social connectivity feature of the devices where they could see each other's progress on the smartphone application, noting that “friendly” competition increased motivation to be more physically active. This study supports using popular m-health technologies for activity tracking among individuals with SMI. These findings can inform the design of weight loss interventions targeting this vulnerable patient population.
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Naslund et al. worked with patients with severe mental illnesses and the mobile app was used exclusively to record physical activity.
“Naslund and colleagues [46, 47] worked with a group of patients with severe mental illnesses from an urban community mental health center enrolled in a group behavioral weight loss program targeting fitness and healthy eating. The mobile app was used exclusively to record physical activity.”
From Mobile Apps for the Management of Comorbid Overweight/Obesity and Depression/Anxiety: A Systematic Review (2020) · cites this paper as [46]
Materials and Methods describes participants with serious mental illness recruited from a community mental health center for a weight-loss program that included exercise and nutrition education. It locates the program in Keene, New Hampshire, rather than describing an urban center. The same section says the smartphone apps tracked activity but also rewarded milestones and let users compare progress through a social network, so they were not used exclusively to record physical activity. (LLM, conf=0.88)
Experimental — accuracy not yet measuredThe two studies by Naslund et al. and the one by Levinson et al. were neither randomized nor blinded.
“The two studies by Naslund and colleagues [46, 47] and the one by Levinson and colleagues [48] were neither randomized nor blinded.”
From Mobile Apps for the Management of Comorbid Overweight/Obesity and Depression/Anxiety: A Systematic Review (2020) · cites this paper as [46]
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“Naslund and colleagues [46, 47] worked with a group of patients with severe mental illnesses from an urban community mental health center enrolled in a group behavioral weight loss program targeting fitness and healthy eating.”
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- Authors
- Naslund, J. A., Aschbrenner, K. A., Barre, L. K., & Bartels, S. J.
- Affiliations
- Dartmouth College (US)
- Cornell University
- Corresponding author
- Naslund, John A.
- Venue
- Telemedicine and e-Health
- Publisher
- SAGE Publications
- Published
- 2015-03
- Volume
- 21
- Issue
- 3
- Pages
- 213-216
- ISSN
- 1530-5627 (ISSN-L)
- 1556-3669
- PMC
- PMC4365437
- OpenAlex
- W2144721638
- Cited by
- 144
- References
- 23
- Citations by year
- 2026: 2 · 2025: 6 · 2024: 8 · 2023: 11 · 2022: 15 · 2021: 11 · 2020: 19 · 2019: 14 · 2018: 16 · 2017: 19 · 2016: 12 · 2015: 11
- FWCI
- 25.35
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Telemedicine Journal and e-Health (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institute of Mental Health (R01MH089811)
- Topics
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Health Sciences › Medicine › Physiology › Physical Activity and Health
- Keywords
- mobile health, activity monitoring, severe mental illness, obesity, weight loss, wearable activity trackers, Fitbit, mobile application, feasibility, acceptability, physical activity, self-monitoring, social connectedness, motivation, lifestyle intervention
- MeSH terms
- Adult, Mental Disorders, Feasibility Studies, Female, Follow-Up Studies, Humans, Male, Middle Aged, Monitoring, Physiologic, Obesity, Physical Fitness, Weight Loss, Exercise, Comorbidity, Body Mass Index, Patient Satisfaction, Telemedicine, Biomedical Technology, Weight Reduction Programs
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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