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Mensajes por telefonía móvil para facilitar el autocuidado de las enfermedades crónicas

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Referencias

References to studies included in this review

Franklin 2006 {published data only}

Franklin V, Waller A, Pagliari C, Greene S. "Sweet Talk": text messaging support for intensive insulin therapy for young people with diabetes. Diabetes Technology and Therapeutics 2003;5(6):991‐6.
Franklin VL, Greene A, Waller A, Greene SA, Pagliari C. Patients' engagement with "Sweet Talk" ‐ a text messaging support system for young people with diabetes. Journal of Medical Internet Research 2008;10(2):e20.
Franklin VL, Waller A, Pagliari C, Greene SA. A randomized controlled trial of Sweet Talk, a text‐messaging system to support young people with diabetes. Diabetic Medicine 2006;23(12):1332‐8. [DOI: 10.1111/j.1464‐5491.2006.01989.x]
Waller A, Franklin V, Pagliari C, Greene S. Participatory design of a text message scheduling system to support young people with diabetes. Health Informatics Journal 2006;12(4):304‐18.

Hanauer 2009 {published data only}

Hanauer DA, Wentzell K, Laffel N, Laffel LM. Computerized Automated Reminder Diabetes System (CARDS): E‐Mail and SMS cell phone text messaging reminders to support diabetes management. Diabetes Technology and Therapeutics 2009;11(2):99‐106. [DOI: 10.1089/dia.2008.0022]

Marquez Contreras 2004 {published data only}

Marquez Contreras E, de la Figuera von Wichmann M, Gil‐Guillen V, Ylla‐Catala A, Figueras M, Balana M, et al. Effectiveness of an intervention to provide information to patients with hypertension as short text messages and reminders sent to their mobile phone (HTA‐Alert) [Eficacia de una intervención informativa a hipertensos mediante mensajes de alerta en el teléfono móvil (HTA‐ALERT)]. Atencion Primaria 2004;34(8):399‐405.

Ostojic 2005 {published data only}

Ostojic V, Cvoriscec B, Ostojic SB, Reznikoff D, Stipic‐Markovic A, Tudjman Z. Improving asthma control through telemedicine: a study of short‐message service. Telemedicine Journal and e‐Health 2005;11(1):28‐35.

References to studies excluded from this review

Anhøj 2004 {published data only}

Anhøj J, Moldrup C. Feasibility of collecting diary data from asthma patients through mobile phones and SMS (short message service): response rate analysis and focus group evaluation from a pilot study. Journal of Medical Internet Research 2004;6(4):e42.

Bauer 2003 {published data only}

Bauer S, Hagel J, Okon E, Meermann R, Kordy H. Experiences with the Short Message Service (SMS) in the aftercare of patients with bulimia nervosa [SMS in der nachstationären Betreuung von Patientinnen mit Bulimia nervosa]. Psychodynamische Psychotherapie 2003;5(3):127‐36.
Bauer S, Percevic R, Okon E, Meermann R, Kordy H. Use of text messaging in the aftercare of patients with bulimia nervosa. European Eating Disorders Review 2003;11(3):279‐90.
Robinson S, Perkins S, Bauer S, Hammond N, Treasure J, Schmidt U. Aftercare intervention through text messaging in the treatment of bulimia nervosa: feasibility pilot. The International Journal of Eating Disorders 2006;39(8):633‐8.

Benhamou 2003 {published data only}

Benhamou PY, Hanaire H, Halimi S, Bosson JL. Web‐based follow‐up using cellular phone in type 1 diabetic patients under insulin pump therapy: the PumpNet study. Clinical Trials.gov Accessed from http://clinicaltrials.gov/ct2/show/NCT00324584 2003. [[ClinicalTrials.gov: NCT00324584]]
Benhamou PY, Melki V, Boizel R, Perreal F, Quesada JL, Bessieres‐Lacombe S, et al. One‐year efficacy and safety of web‐based follow‐up using cellular phone in type 1 diabetic patients under insulin pump therapy: the PumpNet study. Diabetes and Metabolism 2007;33:220‐6. [[ClinicalTrials.gov: NCT00324584]]

Bjerke 2008 {published data only}

Bjerke TN, Kummervold PE, Christiansen EK, Hjortdahl P. "It made me feel connected" ‐‐ an exploratory study on the use of mobile SMS in follow‐up care for substance abusers. Journal of Addictions Nursing 2008;19(4):195‐200. [DOI: 10.1080/10884600802504735]]

Carrasco 2008 {published data only}

Carrasco MP, Salvador CH, Sagredo PG, Márquez‐Montes J, González de Mingo MA, Fragua JA, et al. Impact of patient‐general practitioner short‐messages‐based interaction on the control of hypertension in a follow‐up service for low‐to‐medium risk hypertensive patients: a randomized controlled trial. IEEE Transactions on Information Technology in Biomedicine 2008;12(6):780‐91. [DOI: 10.1109/TITB.2008.926429]

Chang 2008 {published data only}

Chang LW, Kagaayi J, Nakigozi G, Packer AH, Serwadda D, Quinn TC, et al. Responding to the human resource crisis: peer health workers, mobile phones, and HIV care in Rakai, Uganda. AIDS Patient Care and STDs 2008;22(3):173‐4. [[ClinicalTrials.gov: NCT00675389]

Faridi 2008 {published data only}

Faridi Z, Liberti L, Shuval K, Northrup V, Ali A, Katz DL. Evaluating the impact of mobile telephone technology on type 2 diabetic patients' self‐management: the NICHE pilot study. Journal of Evaluation in Clinical Practice 2008;14(3):465‐9.

Ferrer‐Roca 2004 {published data only}

Ferrer‐Roca O, Cardenas A, Diaz‐Cardama A, Pulido P. Mobile phone text messaging in the management of diabetes. Journal of Telemedicine and Telecare 2004;10(5):282‐5.

Fonseca 2006 {published data only}

Fonseca JA, Costa‐Pereira A, Delgado L, Fernandes L, Castel‐Branco MG. Asthma patients are willing to use mobile and web technologies to support self‐management. Allergy 2006;61(3):389‐90.

Gray 2006 {published data only}

Gray R. Impact of peer health workers and mobile phones on HIV care. [ClinicalTrials.gov: NCT00675389]

Hodgson 2005 {published data only}

Hodgson Y. Short Message Service as a support tool in medication adherence and chronic disease management. Health Care and Informatics Review Online 2005;9(3).

Kim 2005 {published data only}

Kim HS. A randomized controlled trial of a nurse short‐message service by cellular phone for people with diabetes. International Journal of Nursing Studies 2007;44(5):687‐92.
Kim HS. Effects of web‐based diabetic education in obese diabetic patients. Taehan Kanho Hakhoe Chi 2005;3(5):924‐30.
Kim HS, Jeong HS. A nurse short message service by cellular phone in type‐2 diabetic patients for six months. Journal of Clinical Nursing 2007;16(6):1082‐7.
Kim HS, Kim NC, Ahn SH. Impact of a nurse short message service intervention for patients with diabetes. Journal of Nursing Care Quality 2006;21(3):266‐71.
Kim HS, Song MS. Technological intervention for obese patients with type 2 diabetes. Applied Nursing Research 2008;21(2):84‐9.
Kim HS, Yoo YS, Shim HS. Effects of an Internet‐based intervention on plasma glucose levels in patients with type 2 diabetes. Journal of Nursing Care Quality 2005;20(4):335‐40.
Kim SI, Kim HS. Effectiveness of mobile and internet intervention in patients with obese type 2 diabetes. International Journal of Medical Informatics 2008;77(6):399‐404.
Yoon KH, Kim HS. A short message service by cellular phone in type 2 diabetic patients for 12 months. Diabetes Research and Clinical Practice 2008;79(2):256‐61.

Lim 2007 {published data only}

Lim FS, Foo M, Kanagalingam D, Lim R, Bahadin J, Tan KL, et al. Enhancing chronic disease management through telecare ‐ the Singapore Health Services experience. Journal of Telemedicine and Telecare 2007;13:73‐6. [DOI: 10.1258/135763307783247257]

Manfrida 2007 {published data only}

Manfrida G, Eisenberg E. Scriptavolant! Use and utility of SMS messages in psychotherapy. Terapia Familiare 2007;85:59‐82.

Newton 2009 {published data only}

Newton KH, Wiltshire EJ, Elley CR. Pedometers and text messaging to increase physical activity: randomized controlled trial of adolescents with type 1 diabetes. Diabetes Care 2009;32(5):813‐5.

Rami 2006 {published data only}

Rami B, Popow C, Horn W, Waldhoer T, Schober E. Telemedical support to improve glycemic control in adolescents with type 1 diabetes mellitus. European Journal of Pediatrics 2006;165(10):701‐5.

Spaniel 2008 {published data only}

Spaniel F, Vohlídka P, Hrdlicka J, Kozený J, Novák T, Motlová L, et al. ITAREPS: Information Technology Aided Relapse Prevention Programme in Schizophrenia. Schizophrenia Research 2008;98(1‐3):312‐7.

Vähätalo 2004 {published data only}

Vähätalo MA, Virtamo HE, Viikari JS, Rönnemaa T. Cellular phone transferred self blood glucose monitoring: prerequisites for positive outcome. Practical Diabetes International 2004;21(5):192‐4. [DOI: 10.1002/pdi.642]

van der Meer 2006 {published data only}

van der Meer V, van Stel HF, Bakker MJ, Roldaan AC, Assendelft WJ, Sterk PJ, et al. SMASHING (Self‐Management of Asthma Supported by Hospitals, ICT, Nurses and General practitioners) Study Group. Weekly self‐monitoring and treatment adjustment benefit patients with partly controlled and uncontrolled asthma: an analysis of the SMASHING study. Respiratory Research 2010;10(11):74.
van der Meer, V. Self‐Management of Asthma Supported by Hospitals, Information and communication technology, Nurses and General practitioners (SMASHING in adults). 2006. [ISRCTN79864465]

Wangberg 2006 {published data only}

Wangberg SC, Arsand E, Andersson N. Diabetes education via mobile text messaging. Journal of Telemedicine and Telecare 2006;12(Suppl 1):55‐6. [PUBMED: 16884582]

References to ongoing studies

Jackson 2006 {unpublished data only}

Improving childhood asthma management through a telemedicine monitoring network. Ongoing studySeptember 2006.

Liang 2009 {unpublished data only}

Using a text‐message system to engage depressed adolescents in cognitive‐behavioral therapy homework.. Ongoing studyFebruary 2009.

Maurino 2009 {unpublished data only}

Effect of daily Short Message System (SMS) reminders on medication adherence to oral antipsychotics in patients with schizophrenia.. Ongoing studyApril 2009.

Møldrup 2007 {unpublished data only}

Assessment of the health‐related effects of compliance optimization in asthma through use of SMS (Short Message System) ‐ a controlled trial.. Ongoing studyNovember 2007.

Rodríguez‐Idígoras 2003 {unpublished data only}

Telemedicine Influence in the Follow up of the Type 2 Diabetes Patient. Ongoing studyOctober 2003.

Shetty 2008 {unpublished data only}

Reinforcement of adherence to prescription recommendations in diabetic patients using Short Message Service (SMS) ‐ a pilot study. Ongoing studyAugust 2008.

Shotan 2006 {unpublished data only}

Short Message Service (SMS) impact on patient compliance receiving long‐term lipid lowering therapy with statins.. Ongoing studyAugust 2006.

van Schayk 2005 {unpublished data only}

A non‐interventional naturalistic project to investigate the effect of the use of SMS text service on treatment adherence in patients treated with Seroquel.. Ongoing studySeptember 2005.

Adler 2007

Adler R. Health care unplugged: the evolving role of wireless technology. http://www.chcf.org/documents/chronicdisease/HealthCareUnpluggedTheRoleOfWireless.pdf. Chronic Health Care Foundation, 2007.

Atun 2006

Atun RA, Sittampalam S. A review of the characteristics and benefits of SMS in delivering healthcare. The role of mobile phones in increasing accessibility and efficiency in healthcare. Vodafone Group Plc, 2006:18‐28.

Atun 2006b

Atun RA, Gurol‐Urganci I. Analysis of calls to NHS Direct. The role of mobile phones in increasing accessibility and efficiency in healthcare. Vodafone Group Plc, 2006:12‐17.

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Characteristics of studies

Characteristics of included studies [ordered by study ID]

Franklin 2006

Methods

RCT (3 arms, study duration 12 months)

Participants

Paediatric patients (aged 8 to 18 years) with Type 1 Diabetes Mellitus receiving conventional insulin therapy attending a clinic in Tayside, Scotland. A total of 92 patients were randomised, of which 89 received their allocated interventions and data were analysed for 90 patients (Group 1 n = 27; Group 2 n = 32; Group 3 n = 31).

Interventions

Participants were randomly assigned to one of three groups: 1) Conventional Insulin Therapy (CIT); 2) CIT with SweetTalk intervention; or 3) Intensive Insulin Therapy (IIT) with SweetTalk intervention. We excluded the third arm from this review.

Sweet Talk is an automated, scheduled text‐messaging system designed to offer regular support to patients with diabetes to optimise their self‐management and diabetes control. Patients contract personal diabetes self‐management goals during the diabetes consultation and, based on these goals and patients' age, sex and diabetes regimen, SweetTalk schedules the automated delivery of a series of appropriately‐tailored text messages, including a weekly reminder of the goal set in clinic, and a daily message providing tips, information or reminders to reinforce this goal. In addition, patients receive occasional text newsletters regarding topical diabetes issues.

Outcomes

Primary outcomes:

  • Glycaemic control, assessed by HbA1c.

  • Behavioural change, measured by a series of validated psychological measures including: self‐efficacy for diabetes score (SED), diabetes knowledge score (DKN), and the diabetes social support interview (DSSI).

Secondary outcomes:

  • Episodes of diabetic ketoacidosis (DKA).

  • Severe hypoglycaemia.

  • Body mass index.

  • Health service utilisation.

Outcome measures were determined at baseline and at the end of the study (12 months).

Notes

Mobile phones and ongoing technical support for the study were provided by Orange.

Risk of bias

Bias

Authors' judgement

Support for judgement

Random sequence generation (selection bias)

Low risk

A computer‐generated allocation sequence was used to assign participants to one of three groups.

Allocation concealment (selection bias)

Low risk

Allocation is said to have been concealed.

Incomplete outcome data (attrition bias)
All outcomes

Unclear risk

At the end of the study (12 months) 4/27 patients were missing from Group 1 (3 discontinued therapy for clinical reasons, 1 withdrew), 6/33 missing from Group 2 (5 discontinued therapy for clinical reasons, 1 moved away), and 5/29 missing from Group 3 (5 discontinued therapy for clinical reasons). The number of patients who discontinued the intervention is comparable in all 3 groups and relatively small. Unlikely to influence results.

Selective reporting (reporting bias)

Unclear risk

Study protocol was not available but data presented match the outcome measures described in the methods section. Likely free of selective reporting.

Other bias

Low risk

Intervention and control groups were comparable at baseline; no other sources of bias were identified.

Blinding of participants and personnel (performance bias)
All outcomes

High risk

Blinding of participants was not possible due to nature of the intervention. Blinding of researchers was not discussed, but likely not done. Unlikely to influence outcome measures.

Blinding of outcome assessment (detection bias)
All outcomes

Unclear risk

No information on blinding of outcome assessment.

Hanauer 2009

Methods

RCT (2 arms, study duration 3 months)

Participants

Diabetes patients (aged 12 to 25 yrs) on insulin treatment (n = 40).

Interventions

The Computerized Automated Reminder Diabetes System (CARDS) includes a web‐based module and a messaging/reminder module designed to run autonomously. Participants log into the system via a secure website where they can customize their schedule for reminder messages, and view, edit, and print their blood glucose (BG) diaries. Participants can opt to receive two daily factoids: one related to diabetes education/nutrition and one with trivia. At a pre‐set time, CARDS sends a reminder to check the BG either by cell phone text message (intervention) or by email (control). After a user submits a BG value, regardless of the result, (s)he receives positive feedback. If the submitted BG value is out of range, CARDS provides a warning to take appropriate action according to the healthcare team's recommendations, and then recheck the BG.

Outcomes

Primary outcomes: Number of BG results submitted.

Secondary outcomes: HbA1c (%).

Notes

Risk of bias

Bias

Authors' judgement

Support for judgement

Random sequence generation (selection bias)

Unclear risk

Participants were "randomized to receive reminders either via cell phone text messaging or by e‐mail". No further information on the method or randomisation was presented.

Allocation concealment (selection bias)

Unclear risk

No information on concealment.

Incomplete outcome data (attrition bias)
All outcomes

Low risk

Data presented for all patients randomised. Presumably no loss to follow‐up.

Selective reporting (reporting bias)

Unclear risk

Study protocol was not available but data presented match the outcome measures described in the methods section. Likely free of selective reporting.

Other bias

Low risk

There were no significant differences between the email (control) and cell phone (intervention) groups at baseline; no other sources of bias were identified.

Blinding of participants and personnel (performance bias)
All outcomes

High risk

Blinding of participants was not possible due to nature of the intervention. Blinding of researchers was not discussed, but likely not done. Unlikely to influence outcome measures.

Blinding of outcome assessment (detection bias)
All outcomes

Unclear risk

No information on blinding of outcome assessment.

Marquez Contreras 2004

Methods

RCT (2 arms, study duration 24 weeks, cluster randomisation)

Participants

Ambulatory hypertension (HT) patients (aged over 18 yrs) whose HT was not well uncontrolled with monotherapy, and who were eligible for treatment with a combination of a single‐dose angiotensin II antagonist and a diuretic (n=67).

Excluded were patients: a) on treatment with 2 or more antihypertensive drugs; b) with secondary HT; c) with known contra‐indications for any of the antihypertensive drugs to be used; d) whose clinical condition might have interfered with the study; e) who were participating in other research studies; f) who lived with a person who was being treated with the same antihypertensive drug; or g) who were unable to give their informed consent.

Interventions

Patients in the intervention group were subscribed to an SMS alerting system programmed to generate random messages. The aim of the messages was to provide information on HT, promote compliance, and good health and dietary habits, and remind patients to take their medication. Two messages were sent per week on randomly chosen weekdays during the 6‐month study period. Receipt of the messages was free to participants in the study and independent of their telephone service operator.

Outcomes

Primary outcomes:

  • Percentage compliance (PC)

    • Monthly;

    • At the end of the study;

    • Cumulative at the end of follow‐up;

    • Change from one follow‐up visit to the next.

Secondary outcomes:

  • Blood pressure;

  • Body weight.

Notes

Risk of bias

Bias

Authors' judgement

Support for judgement

Random sequence generation (selection bias)

Low risk

Researchers were randomised to 1 of the 2 groups with a random number table.

Allocation concealment (selection bias)

Unclear risk

No information on concealment.

Incomplete outcome data (attrition bias)
All outcomes

Unclear risk

After 24 weeks data for 3/36 patients were missing from the control group and 2/36 missing from the intervention group due to lack of record of the number of tablets consumed. The reasons for loss to follow‐up are similar in both groups and unlikely to affect the results.

Selective reporting (reporting bias)

Unclear risk

Study protocol was not available but data presented match the outcome measures described in the methods section. Likely free of selective reporting.

Other bias

Low risk

Intervention and control groups were comparable at baseline; no other sources of bias were identified.

Blinding of participants and personnel (performance bias)
All outcomes

High risk

Blinding of participants was not possible due to nature of the intervention. Blinding of researchers was not discussed, but likely not done. Unlikely to influence outcome measures.

Blinding of outcome assessment (detection bias)
All outcomes

Unclear risk

No information on blinding of outcome assessment.

Ostojic 2005

Methods

RCT (2 arms, study duration 16 weeks)

Participants

Patients with moderate persistent asthma for at least 6 months and being treated with inhaled corticosteroids and long acting beta agonist at a general hospital clinic in Zagreb, Croatia (n=16).

Interventions

Patients in the intervention group were instructed to send their Peak Expiratory Flow (PEF) results daily via text message to a mobile telephone connected to a computer running the Asthma Center 0.90 Software. The software automatically computed maximal, minimal, and mean PEF, PEF variability, and compliance. Patients also received weekly instructions by text message from an asthma specialist on adjustments of therapy and recommended follow‐up based on the PEF values received by text message. Patients in both the intervention and control groups were treated according to GINA guidelines and kept paper asthma diaries.

Outcomes

Pulmonary Function Test results (Forced Expiratory Volume in the first second (FEV1), PEF variability, Forced Vital Capacity); compliance with PEF measurements; asthma symptoms (cough, night symptoms, wheezing, limitation of activity); daily consumption of inhaled medicine and; cost to patient and provider.

Notes

Risk of bias

Bias

Authors' judgement

Support for judgement

Random sequence generation (selection bias)

Low risk

Patients were randomised by computer into either the SMS study group or the control group. Although it is not explicitly mentioned, this suggests use of a random number sequence.

Allocation concealment (selection bias)

Unclear risk

No information on concealment.

Incomplete outcome data (attrition bias)
All outcomes

Low risk

No patient withdrew from the study after enrolment.

Selective reporting (reporting bias)

Unclear risk

Study protocol was not available but data presented match the outcome measures described in the methods section. Likely free of selective reporting.

Other bias

Unclear risk

Intervention and control groups comparable at baseline. However, the study "is limited by the small number of patients and by the particulars of the population studied. The follow‐up period may not have been sufficiently long to reveal all significant differences between the groups."

Blinding of participants and personnel (performance bias)
All outcomes

Low risk

Quote: "The study was not blinded, but this, we believe, has not influenced the outcome. First, compliance was not significantly different in the two groups. Second, the patients in both groups were managed by the same current guidelines."

Blinding of outcome assessment (detection bias)
All outcomes

Unclear risk

No information on blinding of outcome assessment.

Characteristics of excluded studies [ordered by study ID]

Study

Reason for exclusion

Anhøj 2004

No control group

Bauer 2003

No control group

Benhamou 2003

Combines mobile phone and PDA‐based data transmission (not SMS) with SMS based feedback

Bjerke 2008

No control group; qualitative study

Carrasco 2008

Combines mobile phone based (WAP, GPRS and SMS) and Internet‐based data transmission with SMS‐based feedback

Chang 2008

Combines SMS‐based data transmission with regular mobile phone conversation

Faridi 2008

Combines mobile phone‐based data transmission (not SMS) with tailored feedback to patients via SMS

Ferrer‐Roca 2004

No control group

Fonseca 2006

No outcome measures reported after initiation of the study

Gray 2006

Combines SMS‐based data transmission with regular mobile phone conversation

Hodgson 2005

No control group

Kim 2005

Combines PC and mobile phone‐based data transmission with Internet and SMS‐based recommendations

Lim 2007

Combines SMS with Internet‐based data input

Manfrida 2007

No control group; qualitative study

Newton 2009

Combines SMS‐based support with use of open pedometers

Rami 2006

Combines SMS with GPRS data transmission

Spaniel 2008

Combines SMS‐based questionnaire with email alerts and personal follow‐up; No control group

van der Meer 2006

Combines PC or mobile phone‐based data transmission with SMS and Internet‐based feedback

Vähätalo 2004

Combines mobile phone‐based data transmission (not SMS) with SMS‐based feedback

Wangberg 2006

No control group

Characteristics of ongoing studies [ordered by study ID]

Jackson 2006

Trial name or title

Improving childhood asthma management through a telemedicine monitoring network

Methods

RCT (study duration 6 months)

Participants

Participants (aged 3 to 16 yrs) with established doctor diagnosis of episodic or persistent asthma who have had at least one admission to hospital or one episode of acute care in an emergency department or paediatric clinic or general practitioner for asthma requiring steroid rescue within the previous 12 months.

Interventions

Asthma monitoring via mobile phone using SMS

Outcomes

Primary outcomes: Health resource utilisation

Secondary outcomes: School days missed (children) and days off work (parents); Use of medications; Health related Quality of Life (QOL)

Starting date

September 2006

Contact information

Jackson, M, Department of Respiratory Medicine Royal Children's Hospital, Herston Rd, Herston, Brisbane QLD, Australia. [email protected]

Notes

Recruiting at the time of this review.

Liang 2009

Trial name or title

Using a text‐message system to engage depressed adolescents in cognitive‐behavioral therapy homework.

Methods

RCT (study duration 2 month)

Participants

Participants (aged 13 to 17 yrs) with major depressive disorder

Interventions

Homework will be standardised through a primary tool (DTR) for participants to evaluate and respond in writing to their automatic thoughts. The text‐messaging system allows homework to be submitted directly through a cellular phone, includes text‐messaged homework reminder prompts, and collates all homework for therapists to review with participants during therapy sessions. This is assigned and reviewed weekly for 4 weeks.

Outcomes

Primary outcomes: Therapy homework compliance (% homework completed) 

Secondary outcomes: Self reported depressive symptoms (Mood Feeling Questionnaire)

Starting date

February 2009

Contact information

Liang, HC. University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania, United States. [email protected]

Notes

Recruiting at the time of this review.

Maurino 2009

Trial name or title

Effect of daily Short Message System (SMS) reminders on medication adherence to oral antipsychotics in patients with schizophrenia.

Methods

RCT (study duration 6 months)

Participants

Stabilised out‐patients (aged over 18 yrs) with a diagnosis of schizophrenia (DSM‐IV TR criteria) and on oral antipsychotic mono‐therapy.

Interventions

Daily SMS medication reminders

Outcomes

Self‐reported adherence (Morisky Green Questionnaire); Disease awareness (Scale to Assess Unawareness of Mental Disorder (SUMD) Insight Questionnaire); Clinical Global Impression‐Schizophrenia scale score; EQ‐5D score; Attitude towards compliance (DAI‐10).

Starting date

April 2009

Contact information

Maurino, J and Diez, T. AstraZeneca Pharmaceuticals, Spain. [email protected]

Notes

Recruiting at the time of this review.

Møldrup 2007

Trial name or title

Assessment of the health‐related effects of compliance optimization in asthma through use of SMS (Short Message System) ‐ a controlled trial.

Methods

RCT (study duration 90 days)

Participants

Participants (aged 18 to 45 yrs) with asthma. 244 participants enrolled

Interventions

SMS compliance and monitoring system for optimised asthma treatment

Outcomes

Asthma control; EQ‐5D score; Use of health services; Use of preventive medicine

Starting date

November 2007

Contact information

Claus M ldrup, Associate Professor PhD, University of Copenhagen. [email protected]

Notes

Study completed May 2008

Rodríguez‐Idígoras 2003

Trial name or title

Telemedicine Influence in the Follow up of the Type 2 Diabetes Patient

Methods

RCT (study duration 12 months)

Participants

Participants (aged over 30 yrs) with a diagnosis of type 2 diabetes and on Self‐Monitoring Blood Glucose (SMBG) at least 6 months before

Interventions

Participants could send SMBG values to a web page via SMS. The healthcare provider could access this web page to check and, if necessary, return recommendations by SMS

Outcomes

HbA1c level

Starting date

October 2003

Contact information

Rodríguez‐Idígoras, MI. Málaga Health Department, Junta de Andalucia, Spain. [email protected]

Notes

Study completed June 2005. Authors contacted: publication in preparation at the time of this review

Shetty 2008

Trial name or title

Reinforcement of adherence to prescription recommendations in diabetic patients using Short Message Service (SMS) ‐ a pilot study

Methods

RCT (study duration 12 months)

Participants

Participants (aged 30 to 65 yrs) with type 2 diabetes for a minimum period of 5 years and receiving oral hypoglycaemic agents and/or insulin.

Interventions

SMS reminders (once per 3 days) regarding the need for adherence to lifestyle modification and medication.

Outcomes

At baseline and at the end of the study, lipids, and renal function test will be done. A validated questionnaire will be used to assess physical activity, diet habits, adherence to drug prescriptions and frequency of monitoring of blood glucose. Body weight, blood pressure, biochemical variables, scores for diet and physical activity and compliance to drugs, will be compared.

Starting date

August 2008

Contact information

Shetty, SA. India Diabetes Research Foundation (IDRF) and Dr. A. Ramachandran's Diabetes Hospitals. [email protected]; [email protected].

Notes

Recruiting at the time of this review.

Shotan 2006

Trial name or title

Short Message Service (SMS) impact on patient compliance receiving long‐term lipid lowering therapy with statins.

Methods

RCT (study duration 12 months)

Participants

Participants (aged 18 to 80 yrs) discharged from the Intensive Cardiac Care Unit or the Internal Medicine Department following acute coronary syndrome (ACS) events such as unstable angina or acute myocardial infarction who will be prescribed a statin for the first time for preventing further coronary episodes. 120 participants enrolled.

Interventions

Daily SMS medication reminders

Outcomes

Primary outcomes: Number of patients who achieve target LDL goals

Secondary outcomes: Reductions of total cholesterol, LDL, LDL/HDL and CRP; Increase of HDL; Readmissions due to ACS

Starting date

August 2006

Contact information

Shotan, A. Hillel Yaffe medical center. [email protected]

Notes

Ongoing at the time of this review.

van Schayk 2005

Trial name or title

A non‐interventional naturalistic project to investigate the effect of the use of SMS text service on treatment adherence in patients treated with Seroquel.

Methods

Prospective case study

Participants

Participants with schizophrenia or participants experiencing a manic episode associated with a bipolar disorder who were being treated with Quetiapine according to the Core Data Sheet and who were on a stable dosing regime. 128 participants enrolled

Interventions

Daily SMS text messages to enhance patient adherence with medication

Outcomes

Unknown

Starting date

September 2005

Contact information

van Schayk, NPJT, AstraZeneca, The Netherlands

Notes

Study completed April 2008

Data and analyses

Open in table viewer
Comparison 1. Health outcomes

Outcome or subgroup title

No. of studies

No. of participants

Statistical method

Effect size

1 Diabetes ‐ Glycaemic control (HbA1c) Show forest plot

2

88

Mean Difference (IV, Random, 95% CI)

‐0.15 [‐0.77, 0.47]

Analysis 1.1

Comparison 1 Health outcomes, Outcome 1 Diabetes ‐ Glycaemic control (HbA1c).

Comparison 1 Health outcomes, Outcome 1 Diabetes ‐ Glycaemic control (HbA1c).

2 Health outcomes, other (dichotomous measures) Show forest plot

2

Risk Ratio (M‐H, Random, 95% CI)

Subtotals only

Analysis 1.2

Comparison 1 Health outcomes, Outcome 2 Health outcomes, other (dichotomous measures).

Comparison 1 Health outcomes, Outcome 2 Health outcomes, other (dichotomous measures).

2.1 Diabetes ‐ Complications: Diabetic ketoacidosis (DKA)

1

59

Risk Ratio (M‐H, Random, 95% CI)

0.56 [0.10, 3.12]

2.2 Diabetes ‐ Complications: Severe hypoglycaemia

1

59

Risk Ratio (M‐H, Random, 95% CI)

0.21 [0.03, 1.78]

2.3 Hypertension ‐ Blood pressure not under control (no of cases))

1

67

Risk Ratio (M‐H, Random, 95% CI)

0.73 [0.41, 1.29]

3 Health outcomes, other (continuous measures, health outcomes improve with declining mean) Show forest plot

3

Mean Difference (IV, Random, 95% CI)

Subtotals only

Analysis 1.3

Comparison 1 Health outcomes, Outcome 3 Health outcomes, other (continuous measures, health outcomes improve with declining mean).

Comparison 1 Health outcomes, Outcome 3 Health outcomes, other (continuous measures, health outcomes improve with declining mean).

3.1 Diabetes ‐ Body weight (BMI SDS)

1

59

Mean Difference (IV, Random, 95% CI)

0.08 [‐0.35, 0.51]

3.2 Hypertension ‐ Systolic blood pressure (mmHg)

1

67

Mean Difference (IV, Random, 95% CI)

1.10 [‐4.37, 6.57]

3.3 Hypertension ‐ Diastolic blood pressure (mmHg)

1

67

Mean Difference (IV, Random, 95% CI)

1.84 [‐2.14, 5.82]

3.4 Hypertension ‐ Body weight (in kgs)

1

67

Mean Difference (IV, Random, 95% CI)

‐2.76 [‐8.17, 2.65]

3.5 Asthma ‐ PEF variability (%)

1

16

Mean Difference (IV, Random, 95% CI)

‐11.12 [‐19.56, ‐2.68]

3.6 Asthma ‐ Symptoms

1

64

Mean Difference (IV, Random, 95% CI)

‐0.36 [‐0.56, ‐0.17]

4 Health outcomes, other (continuous measures, health outcomes improve with increasing mean) Show forest plot

1

Mean Difference (IV, Random, 95% CI)

Subtotals only

Analysis 1.4

Comparison 1 Health outcomes, Outcome 4 Health outcomes, other (continuous measures, health outcomes improve with increasing mean).

Comparison 1 Health outcomes, Outcome 4 Health outcomes, other (continuous measures, health outcomes improve with increasing mean).

4.1 Asthma ‐ Pulmonary function test (FEV1)

1

16

Mean Difference (IV, Random, 95% CI)

3.00 [‐15.91, 21.91]

4.2 Asthma ‐ Forced vital capacity (%)

1

16

Mean Difference (IV, Random, 95% CI)

‐1.37 [‐16.33, 13.59]

Open in table viewer
Comparison 2. Capacity to self‐manage the condition

Outcome or subgroup title

No. of studies

No. of participants

Statistical method

Effect size

1 Knowledge and management of diabetes Show forest plot

1

Mean Difference (IV, Random, 95% CI)

Subtotals only

Analysis 2.1

Comparison 2 Capacity to self‐manage the condition, Outcome 1 Knowledge and management of diabetes.

Comparison 2 Capacity to self‐manage the condition, Outcome 1 Knowledge and management of diabetes.

1.1 Self‐efficacy for diabetes (SED)

1

59

Mean Difference (IV, Random, 95% CI)

6.10 [0.45, 11.75]

1.2 Diabetes social support interview (DSSI)

1

236

Mean Difference (IV, Random, 95% CI)

4.39 [2.85, 5.92]

1.3 Diabetes knowledge scale (DKS)

1

59

Mean Difference (IV, Random, 95% CI)

‐0.5 [‐1.60, 0.60]

2 Treatment compliance Show forest plot

3

Mean Difference (IV, Random, 95% CI)

Subtotals only

Analysis 2.2

Comparison 2 Capacity to self‐manage the condition, Outcome 2 Treatment compliance.

Comparison 2 Capacity to self‐manage the condition, Outcome 2 Treatment compliance.

2.1 Hypertension ‐ Compliance with medication at six months

1

67

Mean Difference (IV, Random, 95% CI)

8.90 [0.18, 17.62]

2.2 Asthma ‐ Compliance with PEF measurement

1

16

Mean Difference (IV, Random, 95% CI)

4.90 [‐14.82, 24.62]

2.3 Diabetes adherence (Visual analogue score)

1

59

Mean Difference (IV, Random, 95% CI)

6.80 [‐2.58, 16.18]

Open in table viewer
Comparison 3. Health service utilisation

Outcome or subgroup title

No. of studies

No. of participants

Statistical method

Effect size

1 Diabetes ‐ Clinic visit Show forest plot

1

59

Mean Difference (IV, Random, 95% CI)

0.30 [‐0.22, 0.82]

Analysis 3.1

Comparison 3 Health service utilisation, Outcome 1 Diabetes ‐ Clinic visit.

Comparison 3 Health service utilisation, Outcome 1 Diabetes ‐ Clinic visit.

2 Diabetes ‐ Hotline contact Show forest plot

1

59

Risk Ratio (M‐H, Random, 95% CI)

0.32 [0.09, 1.08]

Analysis 3.2

Comparison 3 Health service utilisation, Outcome 2 Diabetes ‐ Hotline contact.

Comparison 3 Health service utilisation, Outcome 2 Diabetes ‐ Hotline contact.

3 Asthma ‐ Utilisation Show forest plot

Other data

No numeric data

Analysis 3.3

Study

Outcome

Mobile phone (n=8)

Control (n=8)

Ostojic 2005

Hospitalisations

2

7

Ostojic 2005

Office visits

21

15



Comparison 3 Health service utilisation, Outcome 3 Asthma ‐ Utilisation.

Risk of bias summary: review authors' judgements about each risk of bias item for each included study.
Figuras y tablas -
Figure 1

Risk of bias summary: review authors' judgements about each risk of bias item for each included study.

Comparison 1 Health outcomes, Outcome 1 Diabetes ‐ Glycaemic control (HbA1c).
Figuras y tablas -
Analysis 1.1

Comparison 1 Health outcomes, Outcome 1 Diabetes ‐ Glycaemic control (HbA1c).

Comparison 1 Health outcomes, Outcome 2 Health outcomes, other (dichotomous measures).
Figuras y tablas -
Analysis 1.2

Comparison 1 Health outcomes, Outcome 2 Health outcomes, other (dichotomous measures).

Comparison 1 Health outcomes, Outcome 3 Health outcomes, other (continuous measures, health outcomes improve with declining mean).
Figuras y tablas -
Analysis 1.3

Comparison 1 Health outcomes, Outcome 3 Health outcomes, other (continuous measures, health outcomes improve with declining mean).

Comparison 1 Health outcomes, Outcome 4 Health outcomes, other (continuous measures, health outcomes improve with increasing mean).
Figuras y tablas -
Analysis 1.4

Comparison 1 Health outcomes, Outcome 4 Health outcomes, other (continuous measures, health outcomes improve with increasing mean).

Comparison 2 Capacity to self‐manage the condition, Outcome 1 Knowledge and management of diabetes.
Figuras y tablas -
Analysis 2.1

Comparison 2 Capacity to self‐manage the condition, Outcome 1 Knowledge and management of diabetes.

Comparison 2 Capacity to self‐manage the condition, Outcome 2 Treatment compliance.
Figuras y tablas -
Analysis 2.2

Comparison 2 Capacity to self‐manage the condition, Outcome 2 Treatment compliance.

Comparison 3 Health service utilisation, Outcome 1 Diabetes ‐ Clinic visit.
Figuras y tablas -
Analysis 3.1

Comparison 3 Health service utilisation, Outcome 1 Diabetes ‐ Clinic visit.

Comparison 3 Health service utilisation, Outcome 2 Diabetes ‐ Hotline contact.
Figuras y tablas -
Analysis 3.2

Comparison 3 Health service utilisation, Outcome 2 Diabetes ‐ Hotline contact.

Study

Outcome

Mobile phone (n=8)

Control (n=8)

Ostojic 2005

Hospitalisations

2

7

Ostojic 2005

Office visits

21

15

Figuras y tablas -
Analysis 3.3

Comparison 3 Health service utilisation, Outcome 3 Asthma ‐ Utilisation.

Summary of findings for the main comparison. Mobile phone messaging for facilitating self‐management of long‐term illnesses

Patient or population: Patients with long‐term illnesses
Settings:  Outpatient services in Scotland, USA, Spain and Croatia
Intervention: Mobile phone messaging support for self‐management of diabetes, asthma or hypertension

Comparison: Usual care, or usual care with self‐management support delivered by email

Outcomes

Impact

No of Participants
(studies)

Quality of the evidence
(GRADE)

Health outcomes: Glycaemic control (HbA1c)

One study found no statistical difference on glycaemic control between groups receiving the intervention or usual care. The other study found mobile phone messaging no more effective than email reminders in achieving glycaemic control. Overall, mean pooled glycaemic control (HbA1C) for the control groups was 9.9 (SD 1.5). In the text messaging groups this was 0.15 units lower (0.77 lower to 0.47 higher).

88
(2 studies)

⊕⊕⊕⊝
moderate1

Health outcomes: Variety of measures

For diabetes and hypertension no statistically significant differences were found between the intervention and control groups on body mass index, weight or blood pressure. For asthma a significant improvement in the text messaging group was found for only 2 out of 4 outcome measures, that is peak expiratory flow variability and pooled symptom score.

142

(3 studies)

⊕⊕⊕⊝
moderate2

Capacity to self‐manage the condition:

Management and knowledge of diabetes

Patients receiving text messaging support showed significantly improved scores on the Self‐Efficacy for Diabetes test and the Diabetes Social Support Interview. It did not, however, result in improved knowledge of diabetes.

59

(1 study)

⊕⊕⊕⊝
moderate1

 

Capacity to self‐manage the condition:

Treatment compliance

Medication compliance in hypertension patients was 8.9% higher (0.18% higher to 17.62% higher) in the text messaging group as compared with the control group. There were no statistically significant effects on compliance with peak expiratory flow (PEF) measurement for asthma patients, or on self‐reported adherence in young people with diabetes. Text message prompts for diabetes patients initially also resulted in a higher number of blood glucose results (46.0) sent back than email prompts (23.5) did.

142

(3 studies)

⊕⊕⊕⊝
moderate2

Participants' evaluation of the intervention

Patients receiving mobile phone messaging support reported improvement in self‐management of diabetes, wanted to continue receiving messages, and preferred mobile phone messaging to email as a method to access the Computerised Automated Reminder Diabetes System.

72

(2 studies)

⊕⊝⊝⊝
very low3

Health service utilisation

Diabetes patients receiving text messaging support made a comparable number of clinic visits and calls to an emergency hotline as patients without the support. For asthma patients, the total number of office visits was higher in the text messaging group, whereas the number of hospital admissions was higher for the control group.

75

(2 studies)

⊕⊝⊝⊝
very low4

*The corresponding risk (and its 95% confidence interval) is based on the assumed risk in the comparison group and the relative effect of the intervention (and its 95% CI).
CI: Confidence interval; RR: Risk ratio.

GRADE Working Group grades of evidence
High quality: Further research is very unlikely to change our confidence in the estimate of effect.
Moderate quality: Further research is likely to have an important impact on our confidence in the estimate of effect and may change the estimate.
Low quality: Further research is very likely to have an important impact on our confidence in the estimate of effect and is likely to change the estimate.
Very low quality: We are very uncertain about the estimate.

1 Number of participants is low in both studies on diabetes.

2 All included trials have a low number of participants.

3 The number of participants is low in both included trials. The outcomes are not compared between the intervention and control groups.

4 Both included trials have a low number of participants. The reasons for clinic or clinic visits and hospitalisations were not known, so the causal link between the intervention and the outcome measures is not clear.

Figuras y tablas -
Summary of findings for the main comparison. Mobile phone messaging for facilitating self‐management of long‐term illnesses
Comparison 1. Health outcomes

Outcome or subgroup title

No. of studies

No. of participants

Statistical method

Effect size

1 Diabetes ‐ Glycaemic control (HbA1c) Show forest plot

2

88

Mean Difference (IV, Random, 95% CI)

‐0.15 [‐0.77, 0.47]

2 Health outcomes, other (dichotomous measures) Show forest plot

2

Risk Ratio (M‐H, Random, 95% CI)

Subtotals only

2.1 Diabetes ‐ Complications: Diabetic ketoacidosis (DKA)

1

59

Risk Ratio (M‐H, Random, 95% CI)

0.56 [0.10, 3.12]

2.2 Diabetes ‐ Complications: Severe hypoglycaemia

1

59

Risk Ratio (M‐H, Random, 95% CI)

0.21 [0.03, 1.78]

2.3 Hypertension ‐ Blood pressure not under control (no of cases))

1

67

Risk Ratio (M‐H, Random, 95% CI)

0.73 [0.41, 1.29]

3 Health outcomes, other (continuous measures, health outcomes improve with declining mean) Show forest plot

3

Mean Difference (IV, Random, 95% CI)

Subtotals only

3.1 Diabetes ‐ Body weight (BMI SDS)

1

59

Mean Difference (IV, Random, 95% CI)

0.08 [‐0.35, 0.51]

3.2 Hypertension ‐ Systolic blood pressure (mmHg)

1

67

Mean Difference (IV, Random, 95% CI)

1.10 [‐4.37, 6.57]

3.3 Hypertension ‐ Diastolic blood pressure (mmHg)

1

67

Mean Difference (IV, Random, 95% CI)

1.84 [‐2.14, 5.82]

3.4 Hypertension ‐ Body weight (in kgs)

1

67

Mean Difference (IV, Random, 95% CI)

‐2.76 [‐8.17, 2.65]

3.5 Asthma ‐ PEF variability (%)

1

16

Mean Difference (IV, Random, 95% CI)

‐11.12 [‐19.56, ‐2.68]

3.6 Asthma ‐ Symptoms

1

64

Mean Difference (IV, Random, 95% CI)

‐0.36 [‐0.56, ‐0.17]

4 Health outcomes, other (continuous measures, health outcomes improve with increasing mean) Show forest plot

1

Mean Difference (IV, Random, 95% CI)

Subtotals only

4.1 Asthma ‐ Pulmonary function test (FEV1)

1

16

Mean Difference (IV, Random, 95% CI)

3.00 [‐15.91, 21.91]

4.2 Asthma ‐ Forced vital capacity (%)

1

16

Mean Difference (IV, Random, 95% CI)

‐1.37 [‐16.33, 13.59]

Figuras y tablas -
Comparison 1. Health outcomes
Comparison 2. Capacity to self‐manage the condition

Outcome or subgroup title

No. of studies

No. of participants

Statistical method

Effect size

1 Knowledge and management of diabetes Show forest plot

1

Mean Difference (IV, Random, 95% CI)

Subtotals only

1.1 Self‐efficacy for diabetes (SED)

1

59

Mean Difference (IV, Random, 95% CI)

6.10 [0.45, 11.75]

1.2 Diabetes social support interview (DSSI)

1

236

Mean Difference (IV, Random, 95% CI)

4.39 [2.85, 5.92]

1.3 Diabetes knowledge scale (DKS)

1

59

Mean Difference (IV, Random, 95% CI)

‐0.5 [‐1.60, 0.60]

2 Treatment compliance Show forest plot

3

Mean Difference (IV, Random, 95% CI)

Subtotals only

2.1 Hypertension ‐ Compliance with medication at six months

1

67

Mean Difference (IV, Random, 95% CI)

8.90 [0.18, 17.62]

2.2 Asthma ‐ Compliance with PEF measurement

1

16

Mean Difference (IV, Random, 95% CI)

4.90 [‐14.82, 24.62]

2.3 Diabetes adherence (Visual analogue score)

1

59

Mean Difference (IV, Random, 95% CI)

6.80 [‐2.58, 16.18]

Figuras y tablas -
Comparison 2. Capacity to self‐manage the condition
Comparison 3. Health service utilisation

Outcome or subgroup title

No. of studies

No. of participants

Statistical method

Effect size

1 Diabetes ‐ Clinic visit Show forest plot

1

59

Mean Difference (IV, Random, 95% CI)

0.30 [‐0.22, 0.82]

2 Diabetes ‐ Hotline contact Show forest plot

1

59

Risk Ratio (M‐H, Random, 95% CI)

0.32 [0.09, 1.08]

3 Asthma ‐ Utilisation Show forest plot

Other data

No numeric data

Figuras y tablas -
Comparison 3. Health service utilisation