mPATH for Cognitive Impairment and Insomnia

Overseen By Julianna Kruemmel

Age: 65+

Sex: Any

Trial Phase: Academic

Sponsor: Johns Hopkins University

Disqualifiers: Sleep apnea, Severe depression, Dementia, others

No Placebo Group All trial participants will receive the active study treatment (no placebo)

Compensation: Varies

Number of Visits: Unknown

Duration: 24 weeks

176 Participants Needed

Trial Summary

What is the purpose of this trial?

Although empirical research suggests that physical activity interventions benefit cognition and sleep in older adults in general, the possible benefit of physical activity is understudied in low-income older adults. The study aims to test the immediate and sustaining efficacy of an mHealth-facilitated Physical Activity Toward Health (mPATH) intervention on cognitive function and sleep in low-income older adults.

Will I have to stop taking my current medications?

The trial information does not specify whether you need to stop taking your current medications. It's best to discuss this with the trial coordinators or your doctor.

What data supports the effectiveness of the treatment mPATH for Cognitive Impairment and Insomnia?

Research shows that mHealth interventions, like mPATH, can improve physical activity and sleep in older adults. A study found that such interventions increased physical activity and improved sleep quality and duration over 24 weeks, suggesting potential benefits for conditions like cognitive impairment and insomnia. ^{1 2 3 4 5}

Is the mPATH treatment safe for humans?

The available research on mHealth interventions, including those similar to mPATH, suggests they are generally safe for humans, as they focus on promoting physical activity and improving sleep without reported adverse effects. ^{1 2 3 6 7}

How is the mPATH treatment different from other treatments for cognitive impairment and insomnia?

The mPATH treatment is unique because it uses mobile health technology to deliver personalized physical activity and sleep interventions through a smartphone app, which includes features like self-monitoring, motivational messages, and phone coaching, making it more interactive and accessible compared to traditional treatments. ^{1 3 5 8 9}

Research Team

Junxin Li

Principal Investigator

Johns Hopkins University

Eligibility Criteria

This trial is for low-income older adults who have trouble sleeping, lead a sedentary lifestyle without much exercise, and can do moderate intensity workouts. It's not suitable for those who don't meet the income criteria or cannot safely engage in physical activity.

Inclusion Criteria

Lack of physical activity/exercise

Low household income

Self-reported difficulty of sleep

Exclusion Criteria

I have a history of brain injury or mental health disorders.

I have sleep apnea that has not been treated.

I have severe depression or anxiety.

Timeline

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

Treatment

Participants receive the mPATH intervention, which includes personalized physical activity plans, training sessions, exercise at home with videos, and biweekly phone coaching over 24 weeks

24 weeks

Biweekly phone coaching

Follow-up

Participants are monitored for changes in cognitive function and sleep efficiency

6 months

Monthly home visits

Extension

mHealth strategies and exercise videos continue to support participant's physical activity

6-12 months

Treatment Details

Interventions

• mPATH

Trial Overview The study tests an mHealth-facilitated program called mPATH designed to improve sleep and cognitive function through increased physical activity. Participants will either receive this intervention or be part of an attention control group for comparison.

Participant Groups

Group I: mPATH Experimental Treatment 1 Intervention

mPATH is a personalized physical activity intervention that includes personalized physical activity plans and training sessions, exercise at home by following personalized exercise videos, and biweekly phone coaching over 24 weeks, supported by wearable devices-enabled mHealth strategies. mHealth strategies and exercise videos will be used to support participant's physical activity during 6-12months

Group II: Education and Social control Active Control 1 Intervention

This attention control group is designed to match the intervention for mPATH's staff-subject interaction duration through monthly home visits in the first 6 months

Who Is Running the Clinical Trial?

Johns Hopkins University

Lead Sponsor

Trials

2,366

Recruited

15,160,000+

National Institute on Aging (NIA)

Collaborator

Trials

1,841

Recruited

28,150,000+

Findings from Research

A 24-week mHealth intervention for 21 older adults led to increased physical activity levels compared to a control group, with improvements observed at 8, 16, and 24 weeks, although activity levels decreased slightly by 24 weeks compared to earlier assessments.

The intervention also significantly improved subjective sleep quality and increased both sleep duration and efficiency by the end of the study, suggesting that mHealth strategies can positively impact both physical activity and sleep in older adults.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

An mHealth-Facilitated Personalized Intervention for Physical Activity and Sleep in Community-Dwelling Older Adults. Li, J., Szanton, SL., McPhillips, MV., et al.[2023]

A study involving 176 participants (9 in focus groups and 167 in an online survey) identified that users with poor sleep prefer personalized features in a sleep app, such as sleep diaries and alarms, to enhance engagement and effectiveness.

Most participants (93%) expressed willingness to try an evidence-based sleep app, highlighting the importance of tailoring app functionalities to user needs, especially for those with severe insomnia.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Exploring User Needs and Preferences for Mobile Apps for Sleep Disturbance: Mixed Methods Study. Aji, M., Gordon, C., Peters, D., et al.[2020]

This systematic review identified only 15 studies evaluating 8 different mHealth apps for sleep disturbances, highlighting a significant gap between the number of available commercial apps and those with evidence-based design and clinical evaluation.

Most apps focused on delivering cognitive behavioral therapy for insomnia (CBT-I), but only one app had a robust randomized controlled trial supporting its efficacy, indicating a need for more rigorous testing and standardization in app development.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Framework for the Design Engineering and Clinical Implementation and Evaluation of mHealth Apps for Sleep Disturbance: Systematic Review. Aji, M., Gordon, C., Stratton, E., et al.[2021]

The iREST app, designed for delivering cognitive behavioral therapy for insomnia (CBTI), was found to be highly usable among 19 Active Duty Service Members and Veterans, achieving an excellent usability score of 85.74, indicating it is an effective tool for improving access to insomnia treatment.

The app's design allows for real-time monitoring and personalized interventions, making it a promising solution for overcoming barriers to traditional CBTI delivery in specialty clinics, while also integrating wearable technology to enhance sleep pattern assessments.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Development of a Just-in-Time Adaptive mHealth Intervention for Insomnia: Usability Study. Pulantara, IW., Parmanto, B., Germain, A.[2023]

The 'Moving Up' app, designed to increase physical activity among underactive older adults, showed good usability and acceptability after beta-testing with 13 participants aged around 71 years.

Feedback from older adults highlighted the importance of personalized messaging and timely notifications, suggesting that involving them in the design process can lead to more effective health interventions.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Preliminary User Evaluation of a Physical Activity Smartphone App for Older Adults. Schepens Niemiec, SL., Wagas, R., Vigen, CLP., et al.[2023]

A study involving 55 participants over 6 months found that individual factors like self-monitoring and goal setting, along with social factors such as user interaction, significantly influenced engagement with a mobile app designed to promote physical activity.

Technological features like automation and personalization enhanced user experience, but limitations in technology were identified as barriers to long-term use, suggesting that interventions need to be tailored to meet diverse user preferences.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Using a Mobile Social Networking App to Promote Physical Activity: A Qualitative Study of Users' Perspectives. Tong, HL., Coiera, E., Laranjo, L.[2020]

The study aims to evaluate the effectiveness of a mobile app called Stay Strong, which tracks physical activity and weight in veterans, comparing the impact of the app alone versus the app combined with personalized health coaching over a year.

With 357 veterans enrolled, the trial will assess whether adding coaching features, such as tailored messaging and phone calls, enhances physical activity levels compared to using the app without coaching.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Understanding the Effect of Adding Automated and Human Coaching to a Mobile Health Physical Activity App for Afghanistan and Iraq Veterans: Protocol for a Randomized Controlled Trial of the Stay Strong Intervention. Buis, LR., McCant, FA., Gierisch, JM., et al.[2020]

The review analyzed 54 studies on mobile health (mHealth) programs promoting physical activity, identifying 31 methods to measure user adherence, which were categorized into 8 indicators across 4 dimensions.

Adherence to mHealth programs is significantly linked to positive health outcomes, and a new framework was developed to help understand and measure adherence, which could guide future research in this area.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Measurement of Adherence to mHealth Physical Activity Interventions and Exploration of the Factors That Affect the Adherence: Scoping Review and Proposed Framework. Yang, Y., Boulton, E., Todd, C.[2022]

The newly developed mHealth application (PAC app) shows promise for enhancing personalized physical activity counseling in primary health care settings, as indicated by focus group discussions with 16 health care providers.

However, successful implementation of the PAC app faces challenges, including barriers to use by providers and the need for patient involvement, highlighting the importance of understanding the service delivery context.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Challenges of Implementing an mHealth Application for Personalized Physical Activity Counselling in Primary Health Care: A Qualitative Study. Wattanapisit, A., Amaek, W., Wattanapisit, S., et al.[2022]

References

1. United States pubmed.ncbi.nlm.nih.gov

An mHealth-Facilitated Personalized Intervention for Physical Activity and Sleep in Community-Dwelling Older Adults. [2023]

2. Canada pubmed.ncbi.nlm.nih.gov

Exploring User Needs and Preferences for Mobile Apps for Sleep Disturbance: Mixed Methods Study. [2020]

3. Canada pubmed.ncbi.nlm.nih.gov

Framework for the Design Engineering and Clinical Implementation and Evaluation of mHealth Apps for Sleep Disturbance: Systematic Review. [2021]

4. Canada pubmed.ncbi.nlm.nih.gov

Development of a Just-in-Time Adaptive mHealth Intervention for Insomnia: Usability Study. [2023]

5. Netherlands pubmed.ncbi.nlm.nih.gov

Preliminary User Evaluation of a Physical Activity Smartphone App for Older Adults. [2023]

6. Canada pubmed.ncbi.nlm.nih.gov

Using a Mobile Social Networking App to Promote Physical Activity: A Qualitative Study of Users' Perspectives. [2020]

7. Canada pubmed.ncbi.nlm.nih.gov

Understanding the Effect of Adding Automated and Human Coaching to a Mobile Health Physical Activity App for Afghanistan and Iraq Veterans: Protocol for a Randomized Controlled Trial of the Stay Strong Intervention. [2020]

8. Canada pubmed.ncbi.nlm.nih.gov

Measurement of Adherence to mHealth Physical Activity Interventions and Exploration of the Factors That Affect the Adherence: Scoping Review and Proposed Framework. [2022]

9. New Zealand pubmed.ncbi.nlm.nih.gov

Challenges of Implementing an mHealth Application for Personalized Physical Activity Counselling in Primary Health Care: A Qualitative Study. [2022]